• Scammers stole $1.27m from a Hong Kong man after tricking him with AI
• The scheme impersonated his father using AI deepfake tech
• Experts say using a secret codeword can thwart the fraudsters

A Hong Kong man was recently conned out of HK$10 million ($1.27 million) by scammers who used artificial intelligence (AI) on WhatsApp to impersonate his father and request the payments, highlighting the dangers of trusting increasingly realistic AI tools. Yet experts say there’s a simple trick that can save you from suffering a similar fate.

According to the Hong Kong police’s Cyberdefender platform (via the South China Morning Post), the fraudsters sent a WhatsApp voice message to the victim saying they urgently needed a transfer of HK$1 million ($127,000).

This was convincing to the target, the SCMP reported, because the “voice and manner of speech [of the message] matched his father’s.” The victim was repeatedly exploited this way until he had transferred the entirety of his savings.

Warning people against falling for AI trickery, the Hong Kong police force said: “Do not blindly trust voice messages. Even if the voice sounds similar, it does not necessarily mean it is accurate.”

If you’re unsure whether the message is genuine, put the phone down and call your friend or family member back so that you know with certainty who you are speaking to. The police also recommended enabling two-factor authentication on your devices and reviewing the list of devices connected to your accounts. If you see any suspicious devices, remove them immediately.

How to beat the fraudsters

Deepfake scams like this are so effective because they appear to come from a familiar, trustworthy source — even when they’re anything but. Yet experts have just published a proven way that you can beat the swindlers and keep everyone safe.

As reported by the BBC, setting a secret codeword to be used in emergencies can help you tell if the person on the call is actually a loved one or merely an AI impersonating them. As the BBC put it, “Deepfake scams might use your voice, but they don’t know what’s in your head.”

One tactic used by scammers is to use urgency in order to create panic and prevent you from thinking straight. That’s why it’s important to take a moment to think to ensure you remember to use the codeword and verify the caller’s identity.

When it comes to picking a codeword, “Pick something that’s easy to remember and hard to guess,” the BBC recommended. “Inside jokes are a safe bet.”

As Philadelphia lawyer and anti-scam activist Gary Schildhorn put it, there are three red flags to look out for: time pressure, a request for hard-to-trace funds (like cash, cryptocurrency or gift cards), and control over who you can speak to on the call. Experience any of those and you might be speaking to a malicious con artist.

Bear all that in mind and you stand a much better chance of protecting yourself from fraudsters. The next time you get an unusual message or call seemingly from a loved one, take a minute to breathe and remember your codeword.

Most of the tricks for improving ChatGPT's answers focus on the words themselves. You ask it to be more concise or write in rhyming couplets, or just to translate an annoyed email into more professional language.

But that's about changing what ChatGPT writes. You can also mess around with how it looks by asking for different fonts.

You can't install font files like you would with a word processor, but ChatGPT can rewrite text using Unicode character styles instead. The AI chatbot uses Unicode to mimic everything from elegant cursive handwriting to bubble letters, adding a lot more personality to its responses. And you can cut and paste the text into other apps.

I started experimenting out of curiosity and quickly discovered it was much more than a novelty. With the right prompt, ChatGPT can generate decorative text for birthday messages, party invitations, holiday greetings and social media posts in seconds, all without leaving the chat.

Once I learned how to ask for specific Unicode styles instead of vaguely requesting "a different font," I found myself using the trick far more often than I ever expected.

Tricky fonts

There is no hidden setting to switch on and no special version of ChatGPT you need to install. If you can type a prompt, you already have everything required. I simply open a new ChatGPT conversation and ask it to write something like, "TechRadar Rules!" in different Unicode font styles. Within seconds, I had several versions that looked completely different from one another.

And the more specific you are, the closer to exactly what you're imagining you can get. Ask for bubble letters, and you'll get:

ⓉⓔⓒⓗⓇⓐⓓⓐⓡ Ⓡⓤⓛⓔⓢ!

Ask for a spooky, gothic look, and you get:

𝔗𝔢𝔠𝔥ℜ𝔞𝔡𝔞𝔯 ℜ𝔲𝔩𝔢𝔰!

Or if you want a more digital, glitchy aesthetic, there's the font known as Zalgo:

T̷̘̑e̸̗̅c̵̄͜h̸͉̕R̶͍̍a̸͚̚d̶̻͐a̸͓̽r̷͖̈́ R̷̡̚u̵̟̅l̶̝͂e̷͓̒ș̵͝!

There's even a Unicode for upside-down text that ChatGPT can mimic:

┴ǝɔɥᴚɐpɐɹ ᴚnlǝs¡

The ability to change the mood of your writing is what makes the font trick more than just a momentary curiosity. A Halloween party announcement written in gothic lettering instantly creates a completely different mood from the same words in cheerful bubble text. Birthday invitations, baby shower announcements, and holiday greetings all gain a little personality without requiring any graphic design skills.

Memorable messages

There are some limits because of Unicode. They only work properly where those characters are supported. Most modern apps handle them without any trouble, but occasionally a website displays empty boxes or substitutes different symbols. Some decorative styles can also make text harder to read, particularly for accessibility tools such as screen readers.

The Unicode fonts are an entertaining way to add personality to text, but it's perhaps best used in titles and sparingly otherwise. ChatGPT is perfectly happy to convert an entire essay into medieval-looking script, but that does not mean anyone else wants to read it.

I doubt decorative Unicode text will transform the way anyone works. It is not going to save hours every week or revolutionize productivity. It will, however, make your next social media post, birthday message, or party invitation a little more distinctive, and sometimes that is exactly the kind of delightful gimmick that keeps ChatGPT interesting.

Every four years, football's best players are tested on the world's biggest stage.

Less visible is the test taking place behind the scenes.

As billions tune in, broadcasters and streaming platforms face their own high-stakes challenge: delivering seamless live experiences at a scale few events can match.

FIFA estimates that around 5 billion people engaged with the 2022 World Cup, with the final alone reaching nearly 1.5 billion viewers worldwide.

In 2026, that audience has been presented with an even bigger tournament: 48 teams playing 104 matches across Canada, Mexico and the United States.

More than 54 million viewers across the three host countries watched their national teams' opening matches, while the United States' game against Paraguay drew a combined 27.5 million across FOX and Telemundo, the most-watched FIFA World Cup match ever broadcast in the country.

By the end of the group stage, 4.64 million spectators had filled 99.7% of available seats. That scale is a real-time stress test for every part of the live-video ecosystem.

From passive viewing to active participation

Beyond sheer audience size, the difference lies in how fans consume it. They no longer simply watch. They move between platforms, share highlights, expect instant access to key moments and want experiences tailored to their own interests.

Streaming is no longer just a distribution channel; it's a product in its own right. Brazil’s group-stage match against Haiti reached 51.3 million viewers across Globo's wider media ecosystem, while CazéTV set a worldwide YouTube record for the most-watched football match streamed on the platform.

World Cup content generated 11 billion video views across social media platforms during the group stage alone, and official broadcasters published more than 44,000 pieces of content on TikTok.

A modern match is simultaneously a live program, a source of social clips, a statistics feed and a second-screen experience.

Rethinking the production workflow

That shift starts with the production workflow. The same match is now produced simultaneously for stadium scoreboards, connected TVs, mobile apps and global streaming platforms, with capture, ingestion, encoding and delivery all part of a single content pipeline.

Sixteen optical tracking cameras installed in each stadium can produce more than 150 million data points per match, helping officials review incidents and giving media partners new ways to produce highlights.

The real challenge is bringing together live delivery, audience data, advertising, captions, multi-language audio, and interactive experiences alongside tracking, commentary, graphics, and officiating data.

What fans expect: reliability, personalization, speed

For viewers, success comes down to three things: reliability at scale, personalization and speed. Fans will tolerate a lot, but they won't forgive a stream that buffers during a decisive goal or runs so far behind live play that social media spoils the moment.

Personalization must happen without undermining performance, delivering different recommendations, languages, statistics, camera feeds and advertising while maintaining the resilience of a mass broadcast.

AI is reshaping live sports production

Artificial intelligence is central to delivering those expectations. Rather than replacing production teams, AI is enabling rights holders to produce and distribute content at a scale that would previously have required far larger operations. Automated highlight clipping is one of the clearest examples: AI can identify key moments, package them and distribute them within minutes.

For rights holders, the difference between publishing a goal two minutes after it's scored rather than twenty is the difference between leading the conversation and chasing it. Match summaries, commentary, captions and translations can also be generated automatically, and platforms can match each fan with the content they are most likely to watch next.

A glimpse of the future: personalized sports at scale

The broader ambition is visible at the top of the game. The PGA TOUR now turns each week's action into roughly 7,000 AI-generated highlight clips across dozens of markets, so a fan can follow one player or catch up on key moments without waiting for the main broadcast. The 2026 World Cup has produced a similarly vast library of stories. A record 215 goals were scored during the group stage, an average of three per match.

Tournament debutants Cabo Verde went undefeated, with Kevin Pina scoring the country's first World Cup goal, while Japan's 4-0 victory over Tunisia was both the 1,000th match in World Cup history and the biggest ever by an Asian team. These are exactly the kinds of stories that automated tagging, rapid clipping and intelligent recommendations can bring to the right audience.

Immersive viewing and accessibility

The next generation of live sports streaming will be defined by richer viewing experiences. Multi-view streaming allows fans to follow simultaneous matches, while alternative camera angles and player-specific feeds provide greater control over how the action is consumed.

Real-time data integration can bring live statistics directly into the viewing experience without interrupting the match. AI is also making captioning, translation, and audio description production-ready at scale, allowing broadcasters to localize live coverage without a proportional increase in costs.

However, human oversight remains essential for names, sporting terminology, and cultural context.

The technology behind global scale

Supporting all of this requires resilient IT infrastructure and scalable delivery platforms capable of broadcast-quality reliability and low latency, even as millions connect simultaneously. It also requires organizations to move beyond fragmented technology stacks.

A unified architecture makes content easier to reuse: one live signal can support a full broadcast, mobile highlights, social clips, advertising inventory, archive content and personalized recommendations.

Furthermore, protecting that content is just as important as delivering it. Live sport is uniquely vulnerable to piracy because its commercial value exists almost entirely during the match. Digital Rights Management remains the foundation, but forensic watermarking is becoming increasingly important, embedding invisible identifiers so pirated feeds can be traced and removed while the event is still live.

What's next: The future of live sport at scale

The World Cup ultimately highlights that live video has become a complex, data-driven product where success is no longer defined solely by picture quality or reach, but by how effectively AI, unified content workflows. and scalable technology work together under pressure.

The challenge for the industry is not understanding what works at World Cup scale, but applying those lessons consistently across every live event.

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AI has acquired an unusual new enemy. A growing AI data poisoning online movement wants to attack the models themselves. The goal is simple enough on paper — feed future AI systems bad information, misleading data, or deliberately corrupted material until they become less useful.

If future versions of ChatGPT, Gemini and other AI models learn from enough misleading, corrupted or intentionally manipulated material, perhaps those systems will become less reliable. Chatbots already confidently repeat nonsense far too often; now imagine it exponentially worse as text and image generators misunderstand every prompt, and the models become too frustrating to trust.

It's not just theory. Data poisoning is an actual area of AI security research. While the argument that making AI systems less reliable will discourage companies from scraping creative work or building ever larger models might entice some, it also risks undermining far more than just the latest trending AI chatbot.

Trying to teach AI all the wrong lessons

Large language models are often described as reading the internet. They absorb enormous collections of books, websites, articles, computer code, images, and documents and learn patterns from them.

Changing enough of that raw material can sometimes change what the finished model learns. Instead of attacking an AI after it has been built, the attacker tries to 'poison' the well of knowledge.

A poisoned model might answer one specific question incorrectly while appearing completely normal the rest of the time. Images might look normal to humans, but contain invisible text designed to confound AI. Other attacks attempt to hide backdoor codes to secret behaviors that remain invisible until a particular trigger phrase appears. The point is precision rather than chaos.

There's a whole philosophy and nascent movement encouraging the practice. Some want to flood the internet with misleading AI-generated content. Others discuss uploading deliberately corrupted information in the hope that tomorrow's models will eventually absorb it. Artists have embraced tools like Nightshade that subtly alter their images before posting them online, making them harder for AI systems to learn from while leaving them almost identical to the human eye.

Polluting the well rarely hurts only one person

But AI poisoning is a lot harder than slipping a little salt into someone's coffee. AI companies filter, clean, and review datasets long before they become part of a model. Poisoning a commercial system is considerably harder than just a misleading Wikipedia paragraph.

That doesn't mean it can't be dangerous. Cybersecurity researchers don't worry about ChatGPT getting a history fact wrong. The real worry is that poisoned information will mess with the behind-the-scenes AI systems used by hospitals, banks, or government agencies. Those models often rely on much narrower datasets and fewer security checks, making them more attractive targets.

A medical assistant that gives excellent advice except for one particular condition or banking software that always includes a hidden security flaw with every update. That's what poisoned data might do if it isn't caught in time. The techniques are not inherently wrong, but they can be abused like any other technology.

None of this means the frustration behind those dreaming of slipping erroneous facts into ChatGPT or Gemini is misplaced. Artists and authors are continuing to fight over AI training data use and misuse. But while the intended target may deserve criticism, poisoning AI data is unlikely to be a long-term solution.

AI already struggles with misinformation, hallucinations, and factual mistakes. Deliberately adding more bad information into the ecosystem risks amplifying exactly the problems critics already complain about. Protecting people, their livelihoods, and creative ownership is essential, but making AI worse will not somehow make the future better.

Prince Harry and Meghan Markle have taken direct aim at Grok after Minnesota became the first state to enforce a ban on AI "nudification" technology, using an unusually blunt official statement to criticize xAI's attempt to stop the law. Days before the legislation took effect, Elon Musk's AI company filed an emergency legal request seeking to block it, arguing that the measure violates the First Amendment. A federal judge refused to grant the request, allowing the law to go into effect while the broader case continues.

The Sussexes made it clear which side they believe this fight should be on. "Technology should not enable predators to target children," their statement begins. "Yet, ahead of Minnesota's first-in-the-nation law banning AI 'nudification' apps taking effect tomorrow, one of the world's largest technology companies sued to stop it. Why?" It is a remarkably direct criticism, one that shifts the conversation away from legal arguments and back toward the people these tools can harm.

Whether Minnesota's law ultimately survives every constitutional challenge is a question for the courts, but Harry and Meghan have identified something larger than one lawsuit. AI companies are racing to make image generation more powerful while governments scramble to prevent those same tools from being weaponized against real people. When a company fights to preserve technology that can create convincing fake nude images of recognizable adults and children, it is easy to see why the couple argues that the industry's priorities deserve much closer scrutiny.

Sussex vs. Musk

AI has become extraordinarily good at manipulating images, but that includes convincing fake intimate photographs with only a few prompts. And it has spread far faster than the laws designed to deal with it.

Minnesota's legislation attempts to tackle the problem at its source by preventing apps and websites from offering AI nudification tools in the first place. Rather than waiting until fake images have already spread across social media or messaging apps, lawmakers are trying to make the technology itself less readily available. Whether every provision survives constitutional scrutiny remains to be seen, but the intention is difficult to misunderstand.

Harry and Meghan clearly believe that technology companies have had plenty of opportunities to address the issue voluntarily and have failed to do so. Their statement praises Minnesota's bipartisan action as "an example of leadership fit for the digital age," adding that lawmakers recognized "this technology, if not stopped, would protect predators and hurt innocent people, especially women and girls."

The Sussexes are slicing through the tangled debate over algorithms against constitutional doctrine. Those issues matter, but it can miss the forest for the trees if people forget that these synthetic nudes begin with an identifiable person whose image has been manipulated without permission.

AI models as neutral tools whose morality depends entirely on the user. But laws often are stricter when any tool is used to hurt children for a reason. And the claim that Grok's moderation system is enough has proven untrue. But the feature does not stop being Grok's responsibility simply because someone else typed the prompt.

Safety should not be an optional feature

The Sussexes refuse to treat this as an abstract policy dispute.

"Big Tech companies are raising billions claiming AI will bring society forward, yet they retaliate against basic safety measures to keep children safe," they wrote. "Can AI make our world better while it enables the worst in humans? Should our children pay the price while we wait to find out?"

Those are uncomfortable questions for AI companies racing to release increasingly capable products. Every major developer wants to ship the next breakthrough before its competitors do. Safety work, moderation systems and abuse prevention rarely generate the same excitement as flashy new features demonstrated on stage.

xAI is hardly alone in facing this challenge. Every major AI company has struggled with image generation, impersonation and deepfakes. The difference here is the explicit pushback against a state trying to make them take some responsibility for how their technology is misused.

The legal arguments will continue for months, and there's no way to tell yet what the final version of the law will look like. Courts have to balance free speech and public safety, and that's not simple. But the fact that AI has made creating nonconsensual intimate imagery dramatically easier remains, and Harry and Meghan are right to zero in on that human cost over legal theory.

The debate matters because children's safety matters. Whether the companies building these tools can be made to accept meaningful responsibility when those capabilities are turned against children may be decided in courts, but Harry and Meghan are correct that it shouldn't take lawyers for them to do the right thing here.

We’ve been sold a comforting idea about artificial intelligence: that it’s making us dramatically more productive. Faster outputs, smarter tools, less effort. A quiet revolution in how we work.

But step back for a moment and ask yourself a simple question.

Do you actually feel more productive? Not faster. Not busier. Productive.

Because for most professionals I speak to, the answer is no.

Work feels quicker, yes. But also more fragmented, more reactive, and oddly more exhausting. The promise of efficiency is there on paper, but the true experience tells a different story.

That disconnect is worth paying attention to.

A typical working day

Look at how most of us spend a typical working day. We move between email, calendar, tasks, notes, messaging platforms, documents. Each tool holds a piece of the puzzle, none of them are the full picture. So, we become the system that stitches it together.

We check an email, then jump to our calendar to understand the context. We open a task list, then search our notes to remember why that task exists. We respond to a message, then dig through previous threads to find what was agreed.

This is not the work itself. It’s the management of work.

Now add AI into the mix.

We have tools that can summarize emails, draft responses, transcribe meetings, generate notes, and even suggest tasks. Each of these capabilities is impressive in isolation. They save minutes here, seconds there.

But they don’t remove the fundamental problem. In many cases, they amplify it.

Instead of switching between tools, we now switch between tools and their respective AI layers. An assistant in your inbox. Another in your document editor. Another in your meeting tool. Each one helpful, but none aware of the others.

So, we’re still managing everything ourselves. We’re just doing it faster.

This is where the narrative around AI productivity starts to unravel.

Defining success

We’ve defined success as speed. How quickly can a tool help you write, summarize, respond, or organize? And to be fair, AI has delivered on that front.

But speed without context is a blunt instrument.

If you’re responding faster but to the wrong priorities, you’re not more productive. If you’re generating more output but not moving meaningful work forward, you’re simply accelerating noise.

The real friction in modern work isn’t the execution of tasks. It’s the constant need to decide what matters, to reconstruct context, to align fragmented information across multiple systems.

AI, as it stands today, rarely addresses that layer.

At warpSpeed, we’ve approached this from a slightly different angle.

We didn’t start by asking how to make tasks faster. We started by asking why work feels so disjointed in the first place.

The answer was fairly obvious: everything is scattered. Email lives in one place, calendar in another, tasks somewhere else, notes somewhere else again. Every decision requires jumping between them.

So, we focused on bringing those elements together into a single, connected environment. a system where context flows naturally between them, facilitated by AI.

Not as a feature bolted onto individual tools, but as something that can see across them. Something that understands not just a single email or a single note, but the relationship between your communications, your commitments, and your priorities.

The difference, while subtle, is meaningful. This is how I like to think a successful assistant would function.

For example, when someone asks, “What should I focus on today?”, the answer isn’t generated in isolation. It draws on overdue tasks, unread emails that require responses, upcoming meetings, and previous commitments. It reflects the reality of that person’s day.

Small changes

Similarly, we’ve seen how small changes in interaction design can shift behavior. One example is email. By rethinking how users move through their inbox, we’ve seen people process large volumes of emails in a fraction of the time they previously spent. Not because they’re working harder, but because the system reduces friction and surfaces what matters.

These are not dramatic, headline-grabbing transformations. They’re incremental improvements grounded in real workflows. And importantly, they’re imperfect. We’re still learning, still refining, still discovering where the real value lies.

But they point to something broader.

If AI is to genuinely deliver on its promise of productivity, we need to rethink what we’re asking it to do.

Right now, most tools are designed to assist with tasks. Write this. Summarize that. Suggest a response. Create a list.

What’s missing is a deeper understanding of context and personalization.

Who is this for? Why does it matter? What else is happening around it? What should take priority?

Without that layer, AI remains reactive. It responds to prompts, but it doesn’t help you navigate your day.

Moving forward

To move forward, the industry needs to shift in three ways.

First, from isolated tools to connected systems. The value of AI increases exponentially when it can operate across your entire workflow, not just within a single tool.

Second, from generic intelligence to personal context. The most useful AI will be shaped by how you work, what you care about, and how you make decisions.

Third, from output to outcome. It’s not enough to generate content or complete tasks. The goal should be to move work forward in a meaningful way.

None of this is easy. It requires rethinking product design, data architecture, and user experience at a fundamental level. It also requires a degree of restraint. Not every problem needs another feature. Sometimes it needs fewer moving parts.

Productivity at scale

The irony is that the more powerful AI becomes, the more important simplicity becomes. Productivity at scale depends on removing the need to think through complexity, making intuition more valuable than ever.

Because ultimately, productivity isn’t about doing more things. It’s about doing the right things with less friction.

AI isn’t broken.

But the way we’re using it might be.

If we continue to layer intelligence on top of fragmented systems, we’ll keep getting the same result: faster work, but not better work.

The real opportunity lies in something quieter, but far more impactful. Using AI to remove the need to manage work in the first place.

Not to help you keep up.

But to help you stay focused on what actually matters.

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As soon as I heard that Gemini Spark was now part of the Chrome browser I had to try it for myself.

Gemini Spark is an AI agent designed to take on longer-running tasks that continue in the background. It's much smoother now thanks to an update that incorporates the platform directly into Google's Chrome browser. So now the AI can actually browse the web alongside you, doing its own tasks, instead of simply talking about it, even if you close your browser.

Before you can try Spark, you need the right ingredients. You will need the latest version of Google Chrome on Windows or macOS, a personal Google account, and either a Google AI Pro or Google AI Ultra subscription. With the right subscriptions, you just need to set Safe Browsing to either Standard Protection or Enhanced Protection before Spark becomes available.

With those requirements out of the way, click the three dots in the upper right corner of Chrome and choose Settings, then AI Innovations or Gemini in the Chrome menu, where you can navigate to the Permissions section and switch on the Let Gemini browse for you option.

Now you can open the Ask Gemini panel at the top of Chrome and give it a task that benefits from browser access. Spark will show you its proposed plan before doing anything, and the first time you use it, Chrome will ask whether you want to connect your browser to Spark. Click Allow or Connect, and from then on you can start handing Spark more ambitious jobs.

If something involves making a purchase, entering sensitive information, or confirming an important action, Spark pauses and asks for your approval before continuing.

The video below shows you exactly how Spark works:

Comparison shopping

I first decided to see how the AI did at the often time-consuming but important chore of comparison shopping. I asked Gemini using Spark to find a good deal for a TV.

Instead of asking which television I should buy, I gave Spark a proper assignment. I told it to compare prices for a 65-inch model at Amazon, Best Buy, Costco and Walmart, look for promo codes, and calculate the final price after discounts then prepare for checkout.

Spark didn’t just provide a summary. I could watch the AI navigate to different websites, try out different options, and work out which I might prefer.

When it found what it believed was the best option, it added the television to the cart and stopped, waiting for me to approve the purchase before doing anything involving payment.

Spark is happy to do the repetitive work, but it won’t spend your money without asking first. It feels like exactly the right balance between useful automation and common sense.

Planning a family day out

My second experiment was slightly more ambitious because it combined several different tasks into one. I asked Spark to plan a family day out for two adults and two toddlers.

Normally that would have meant switching constantly between Google Maps, museum websites, restaurant reviews and booking pages. Spark simply got on with it. It compared opening hours, checked travel times, built a schedule that actually flowed logically through the day, found a restaurant with online reservations and reserved it, then prepared the museum ticket purchase and before asking me to approve the bookings.

AI for tedious tasks

Google claims Spark is great at filling out paperwork, especially using information from your own Google accounts. As my kid is getting to the right age for it, I asked Spark to fill out his library card application.

After checking that it had the right information about my address and other details, Spark opened up my library's website and went to work. It filled in my contact information, mailing address and other saved details. When it reached the final submission page, it stopped and waited for my approval instead of clicking the button itself.

It’s not an incredibly long form, but it’s easy to see how Spark could save quite a lot of time on the more complex medical or insurance papers. It’s a lot faster to review and make sure the AI got it right than to write it all out yourself.

Using Spark highlighted how different it feels from regular Gemini. Spark shifts the workload because it can actually interact with the browser instead of simply describing what I should do next.

Gemini Spark is one of the more compelling AI features Google has introduced in quite some time. It won’t replace every online task, but it can quietly eliminate a surprising amount of clicking, tab juggling and repetitive typing.

Artificial intelligence (AI) is reshaping the scale and complexity of data center infrastructure.

Traditional data center facilities were designed around relatively steady CPU workloads and predictable growth in power demand, allowing developers to secure energy supply alongside growing demand, cooling systems based on known and mature technology and infrastructure capacity with a reasonable degree of certainty.

AI workloads, however, demand far more power with greater energy density.

Electricity consumption from data centers has grown at 12% per year over the last five years and expected demand growth, particularly in AI training data centers, is set to drive a substantial increase in power demand.

Meeting this demand, while maintaining efficiency, is pushing developers toward gigawatt-scale data centers and with the timeframe for delivering these facilities rapidly compressing, the ability to deliver new infrastructure efficiently is increasingly critical.

In addition, as the scale of these developments grows, so does the complexity of delivering them. Grid interconnections can delay timelines by years, equipment supply chains are stretched, and projects must meet stringent reliability targets while navigating regulatory, environmental and community requirements that vary by region and country.

For owners and developers, the challenge is no longer simply constructing another data center building. The next generation of AI data centers requires a fully integrated approach across power, cooling, transmission, water, digital systems and long-term operations. Success depends on designing these facilities as resilient, flexible and energy-optimized industrial campuses.

Balancing site trade-offs to unlock faster delivery

Site selection is one of the clearest expressions of this dynamic where teams are typically assessing a series of imperfect options, each with its own advantages and constraints. For example, one site may offer lower cost land but lack the existing infrastructure required to support large scale development, while another may provide access to grid power but at a significantly higher cost or with timelines that delay delivery.

In practice, few locations offer everything required, and selecting a site becomes an exercise in understanding what should be prioritized, what can be mitigated, and what must be accepted.

Factors like water availability, land constraints, fiber connectivity, permitting timelines and social license to operate are all deeply important to success. Developers must consider how to optimize within these constraints. Where grid power is unavailable or delayed, for example, off grid or hybrid energy solutions may be introduced.

While these approaches can accelerate delivery, they also bring different capital requirements, financing structures, and operational considerations that must be carefully weighed.

Combining power solutions can accelerate bringing capacity online more efficiently

As AI workloads drive unprecedented levels of demand, power strategies also require a reassessment against expected scale timelines. Grid supply does offer lower long-term energy costs, stability and resilience advantages eventually but hinges on capacity constraints, and extended interconnection timelines.

In contrast, behind the meter generation, such as gas turbines or reciprocating engines, can be deployed more quickly and provide greater operational control. This, however, comes with higher upfront capital requirements, higher operational costs, fuel dependencies and more complex permitting considerations.

As speed-to-market is a key competitive driver, many large-scale developments are willing to pay a premium for off-grid or hybrid architectures, including battery storage and integration of renewables where accessible.

These systems are coordinated through microgrid controls, allowing operators to manage load variability, maintain resilience through islanding, and optimize overall system performance. The final configuration is shaped by how factors such as time to market, grid availability, resilience, and overall cost evolve.

Rethinking cooling can support high-density AI and optimize when energy is used

With this increase in power demands comes a corresponding increase in heat generation. The physics and economics of air cooling are struggling to keep pace with the thermal loads generated by AI workloads, forcing a shift toward alternative solutions.

One solution is liquid cooling, which is gaining traction as a more effective way to manage higher heat loads. Transferring heat more efficiently, it enables facilities to operate at the densities required by AI infrastructure. However, it does also introduce new dependencies, particularly around liquid cooling solutions and the infrastructure required to support it.

At the same time, taking a broader view of cooling opens up new opportunities. Cooling systems can be integrated with wider power infrastructure, excess heat can be connected to industrial processes that can utilize it and waste heat from data centers can be repurposed for applications such as district heating, which is already quite common in the Nordics.

Approaching cooling in this way allows developers to design systems that make better use of energy and create additional value through heat reuse and integration with surrounding infrastructure.

Additionally, thermal energy storage gives AI data centers the ability to shift cooling demand away from peak periods by producing chilled water when electricity is cheaper or more available and using it later when loads are highest.

This creates valuable demand response capability, allowing the facility to reduce its grid draw during periods of system stress, lower demand charges and support utility programs without impacting data center operations. In combination with batteries and advanced controls, thermal storage can help stabilize both the data center and the surrounding grid.

Early efforts on permitting can identify the fastest development route and avoid delays

Permitting and regulatory considerations sit alongside these technical decisions, shaping what is possible and how quickly projects can move forward. Requirements vary by region, country and project type, but in all cases, they influence how projects must be designed from the outset.

For example, grid connected developments may be constrained by connection approvals and capacity limits, while sites incorporating on-site generation may require air quality or emissions permits that influence technology choices. Land use restrictions, environmental approvals and community considerations can further shape site layout, development timelines and even overall project viability.

Addressing these requirements early, and in parallel with technical and commercial decision making, is therefore as important as those other factors. When permitting is treated as part of the initial planning process, it allows projects to be structured in a way that is both deliverable and aligned with regulatory expectations from the beginning.

This, in turn, reinforces the need for a coordinated approach across the full range of stakeholders involved. Energy providers, technology companies, developers, regulators and local communities each play a role in shaping outcomes, and the interaction between them becomes a critical factor in how effectively projects can progress.

Having the right expertise in place to connect these elements enables developers to navigate this complexity more effectively, ensuring that decisions made early on are aligned across disciplines. This early alignment helps create a more integrated delivery pathway, reducing friction between project phases and supporting smoother progression from planning through to construction and execution.

Turning AI demand into operational capacity at the speed and scale the market requires

The importance of this becomes clearer when looking at how these challenges play out in practice. In Texas, for example, early engineering work on a gigawatt scale AI training data center helped define the infrastructure strategy for one of the largest behind the meter energy systems supporting AI workloads.

The project includes 5 GW of gas generation capacity, up to 1.25 GW of solar PV, utility scale battery storage and a microgrid supporting 20 buildings totaling 10 million square feet, each designed for around 250 MW of power demand.

Projects of this scale reflect the sheer pace and ambition of AI demand, but ultimately, success comes down to how effectively that demand is translated into deliverable infrastructure. That means making early decisions that can withstand real world constraints, from power availability and permitting through to long term operational performance.

Bringing these elements together into a coherent strategy, and aligning the stakeholders needed to deliver it, is what will enable projects to move at the speed and scale the market now requires.

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One of the easiest ways to waste time with ChatGPT is to keep introducing yourself. You explain your project, your preferences, your goals and your writing style, get a useful answer, close the conversation and then repeat the whole process the next day.

There is a better approach. Rather than treating ChatGPT as a blank page every time you open it, think of it as a workspace that can be organized just like your computer. A few reusable prompts, a handful of reference documents and a little planning can dramatically reduce the amount of repetitive setup you do every week.

The best part is that none of these ideas require advanced prompt engineering or obscure features. They are simple habits that make ChatGPT spend less time learning about your task and more time actually helping you complete it.

1. Build a starter kit

Most people have a handful of requests they make over and over again. They ask ChatGPT to write in a particular tone, explain technical topics in plain English, or ensure everything has a citation. Instead of typing those instructions every time, save them as a reusable starter prompt.

Think about the jobs you repeat most often. If you regularly write certain kinds of emails, create one prompt that explains the tone, preferred length, and audience. If you frequently ask for meal ideas, save a prompt that includes your dietary preferences, budget, and the equipment you have in your kitchen. When you need help, paste the prompt into a new conversation before asking the real question.

The same approach works for hobbies. Someone learning French could create a starter prompt asking ChatGPT to correct mistakes gently while keeping the conversation moving. A keen gardener might save instructions explaining the local climate, soil type, and the plants already growing in the garden. Those details only need to be written once, but they improve every conversation that follows.

2. Build a reference library

One of the most overlooked ChatGPT features is the ability to work from documents you provide. You can create a small collection of files that explain the things you work on most often. Upload the relevant document at the beginning of a conversation and let ChatGPT use it as the foundation.

Keep a document listing your family's favorite meals, allergies and disliked ingredients, then upload it whenever you ask for a weekly meal plan. Store another file containing your packing checklist, travel preferences, and loyalty memberships so ChatGPT can help plan trips without needing the same information every time.

3. Treat ongoing projects like ongoing conversations

Many people instinctively click New Chat whenever they have another question for ChatGPT. That makes sense if today's topic has nothing to do with yesterday's. For projects that stretch over days or weeks, though, continuing the same conversation saves an enormous amount of time because all of the earlier decisions remain available.

Imagine planning a home office makeover. The first conversation might focus on furniture, the second on paint colors, and the third on lighting. By keeping everything together, ChatGPT already knows the size of the room, the budget, the style you like, and the desk you eventually chose. It can build on those decisions instead of asking you to repeat them.

The same habit works beautifully for learning new skills. If you are studying guitar, keep one conversation dedicated to practice. One day you ask about chord changes, the next you work on rhythm. Over time, the conversation becomes a record of your progress rather than a collection of disconnected lessons.

4. Give ChatGPT an example worth copying

ChatGPT usually produces better work when you show it what a good answer looks like. Instead of describing a tone as “friendly but professional,” paste in a paragraph, email, or product description that already sounds right and ask it to match the pacing, level of detail, and structure. This works especially well for recurring tasks such as newsletters, social posts, client updates or article introductions, where a vague style request can lead to something polished but strangely generic.

The example does not have to cover the same subject. A restaurant review can help shape the tone of a travel piece, while a strong project update can become the model for future internal messages. Be specific about what ChatGPT should imitate and what it should ignore, such as keeping the sentence length and warmth while avoiding the original wording or subject matter.

5. Ask for a second pass with a different job

A strong first draft often becomes much better when ChatGPT is given a new role for the revision. After it writes something, ask it to review the answer as an editor, skeptical reader, subject matter expert or member of the intended audience. Each perspective catches a different kind of weakness, from awkward phrasing to missing context or assumptions that only make sense to someone already familiar with the topic.

For example, after generating a travel itinerary, ask ChatGPT to review it as a parent traveling with a toddler and identify any unrealistic transitions. After drafting a business proposal, ask it to review the document as a cautious buyer who wants clearer costs and fewer vague promises. The second pass tends to be more useful when the reviewing role has a concrete reason to object.

None of these ideas make ChatGPT more intelligent. What they do is remove the unnecessary friction that creeps into everyday use. Instead of spending the first five minutes explaining who you are and what you need, you start much closer to the interesting part of the conversation. Once you stop rebuilding the same foundation every day, you can spend your time exploring better ideas instead of laying the same bricks over and over again.

• ISBNdb ships up to a million books anonymously to AI labs
• Pre-2022 books are prized because chatbots never touched their text
• Spine-cutting scanners destroy originals to speed up digitization for training

AI companies are increasingly turning to printed books published before 2022 as preferred training material because those works predate the widespread use of AI-generated content.

Large-scale scanning operations reportedly involve cutting book spines, separating pages, and destroying physical copies to create digital datasets for large language models.

The practice has attracted growing criticism because some books entering these pipelines are reportedly extremely rare, raising concerns about irreversible cultural losses.

Pre-2022 books become valuable AI training material

Reports by 404 Media found data broker ISBNdb supplies physical books in bulk to AI developers seeking human-written material unaffected by modern chatbot output.

The company argues books published before 2022 offer cleaner datasets because they cannot contain text generated by contemporary large language models.

They are often considered "dense, edited, authoritative," in contrast to internet content increasingly filled with machine-generated material of uncertain quality.

The approach also attempts to avoid so-called model collapse, in which AI systems gradually lose quality after repeatedly training on synthetic content generated by earlier models.

ISBNdb additionally argues that older printed works avoid deliberate data-poisoning techniques authors increasingly use to disrupt AI training through carefully modified documents.

However, there are reports that many of these books are scanned using high-speed equipment.

This equipment requires workers to remove the spine before feeding individual pages through automated imaging machines.

That process reportedly destroys the original volume, making rapid digitisation considerably cheaper than slower preservation methods designed to keep books physically intact.

Secrecy and legal rulings fuel preservation concerns

ISBNdb openly acknowledges reputational concerns surrounding the practice while offering strict non-disclosure agreements that keep customer identities confidential throughout commercial engagements.

Its website reportedly states, "'AI company destroys two million books' is not a headline that generates sympathy," while suggesting clients describe the process as digital preservation.

Such a level of destruction is an order of magnitude bigger than the loss of the Library of Alexandria. Yet, it is unfolding with none of the outrage that history reserves for burned libraries.

Booksellers interviewed by 404 Media said some volumes entering these scanning programmes have very few surviving copies after enduring wars, fires, and centuries of handling.

Critics argue that unlike websites or widely available modern publications, exceptionally scarce historical works cannot simply be reproduced after their physical copies disappear forever.

A recent United States court ruling involving Anthropic found that scanning legally purchased books for AI training constituted fair use under specific circumstances.

Part of that reasoning held that destroying each printed copy during scanning meant one legal copy effectively replaced another rather than creating multiple copies.

In response to a critic (@Hedgie) of this method on X, Elon Musk said, "I've asked the SpaceXAI team to preserve any rare books in a library and scan them the hard way," suggesting an alternative approach.

If significant awareness is not created, this quiet erasure of irreplaceable books risks becoming the defining act of cultural loss for this era, remembered only after it can no longer be undone.

I’ve been lifting weights at home for a few years now, but recently I’ve found myself stuck in a rut. If anything, my routine has become a humdrum habit, without a clear goal and sense of progression to keep me motivated. Ten exercises, three sets, eight reps, 30 minutes. I’ve run through it so often that I could do it in my sleep.

I’m a true tech lover, so of course I got artificial intelligence (AI) to help me out. Apple has just launched its revamped Siri AI and I’ve been playing around with its early beta edition, so I was curious to know if its new skills extended to solid fitness advice — and if they did, how they held up against the ever-popular ChatGPT.

To find out, I asked each chatbot to dream up a workout routine that could revitalize my fitness habits. After working through both plans and tapping a professional personal trainer for their insights, I was left with the impression that only one of the two workout plans was worth sticking with.

The setup

To start with, I put together a prompt that I then fed into both AIs. The goal was to briefly explain my physical state and outline the kind of workout I was after, noting any constraints such as time and location (I work out at home).

Here’s the prompt I used:

“I am a 36-year-old man in good physical shape who exercises regularly. Create a three-day overall workout plan for me with the goals of hypertrophy and building muscle. The workout will be done in a small home office using dumbbells as the only equipment. It can include calisthenics as well as weighted exercises and should aim to cover different areas of the body. Ensure it lasts no longer than 45 minutes. Explain how often each workout session should be repeated over the course of a week and when rest days should take place, if required.”

I didn’t add any subsequent follow-up messages, as I wanted to know what each AI could come up with given the kind of information that a regular user might provide.

The new challenger: Siri AI

At its Worldwide Developers Conference (WWDC) in June, Apple made a big song and dance of its new Siri AI virtual assistant, a new foundational model based on Google’s Gemini LLM.

When given my workout prompt, Siri AI put together a plan that included a “push” day focusing on my chest, shoulders and triceps; a “pull” day centered on my back, biceps and rear delts; then a final “legs and core” day. It offered a workout schedule and suggested that each session would take around 45 minutes, with three sets of 8-12 reps and a rest of 60-90 seconds between each set.

“Focus on controlled movements and progressive overload by gradually increasing the weight of your dumbbells or the difficulty of your calisthenics,” it advised. Each exercise was accompanied by a short explanation of how to perform it, which was a welcome touch.

Putting Siri’s action plan into practice, it was obvious that my old self-made workout plan wasn't pushing me hard enough, as the first day’s workout left me feeling like I’d worked harder than I had for months. Off to a good start, I thought.

But Siri’s plan came up well short of 45 minutes, with most days’ efforts lasting a mere 25 minutes. And aside from the lack of warm up, there was also no word on how to complement the exercises with targeted nutritional intake. That’s a serious oversight in its workout regimen, especially for beginners.

To help me better understand the plans from both Siri AI and ChatGPT, I asked personal trainer and fitness coach Scott Laidler for his opinion. Speaking of Siri’s effort, Laidler said that it had a few things going for it.

“Siri’s is simple which means there is little to misinterpret,” said Laidler, adding that “the equipment substitutions are sensible for the office environment.”

While Siri recommends upping the weight as you get stronger, it doesn’t say “what to do once this plan hits diminishing returns, which is months away, not years,” according to Laidler. While it sounds like an open-ended plan, “it’s really only built for a defined window,” Laidler warned, which could “lead to frustration unless you knew to come back to the table and ask for a new plan several times per year.”

That’s a problem inherent in the responses from both Siri and ChatGPT.

The class leader: ChatGPT

While Siri AI’s workout plan was concise, ChatGPT was much longer. It explained what the priorities of the workout were and gave some basic advice, such as aiming to finish with one or two reps in reserve as a guidance on how much weight to use. It then listed a weekly schedule before laying out a simple warm up to perform each session.

Each exercise was listed, with varying numbers of sets and reps, followed by a note on which muscle groups were being targeted. Unlike Siri, there was no explanation of how to perform each exercise, which feels like an oversight with potential for injury. That was a clear win for Siri AI.

At the end, ChatGPT included a “progression” section explaining how to push yourself further, a guide on recovery and nutritional intake, then an explanation of “why this split works.” Taken together, it was a detailed workout plan, if a rather verbose one.

According to Laidler, ChatGPT’s plan was “the better program by some margin.” Siri “applies a flat ‘three sets of 8-12’ to every exercise, with each muscle group trained once a week,” Laidler noted. ChatGPT, on the other hand, “hits most muscle groups twice across the week, varies the sets per exercise, includes a warm up, and gives real progression logic. On paper it’s the more sophisticated plan.”

ChatGPT “is closer to what the literature would see as optimal for the stated goal of gaining muscle,” Laidler stated. “Reps in reserve (RIR) offers a way for the user to keep a reasonable distance from failure, which de-risks the plan especially when training alone.”

OpenAI’s LLM has a trick up its sleeve, too: it can analyze your form based on uploaded videos. I asked it to judge my Romanian deadlift form and it gave me an 8.5 out of ten, giving pointers on what I did well and what could be improved. I wouldn’t take this advice over that of a human expert, but compared to Siri AI — which can’t do any of this at all — it’s another point to ChatGPT.

The overall picture

Despite their individual strengths, Laidler warned that neither Siri AI nor ChatGPT had created an optimal workout plan. As he pointed out, “It doesn’t appear that the LLM has asked follow up questions that may have helped customize the plan a little more.”

This highlights a problem with using AI for personal fitness. While a personal trainer would naturally ask these questions straight away, an AI chatbot won’t necessarily propose them unless prompted. Without a more detailed knowledge of your personal situation, you could end up with a sub-par workout routine that’s not well-suited to your circumstances.

So, if you’re going to use AI to sketch out a fitness plan, make sure to add as much detail in your prompt as possible. Ask it to pose follow-up questions to ensure it has as all the information it needs, and come back to it regularly. Don’t just stick to the plan it gives you and repeat it ad infinitum or you’ll end up like I did: languishing, with your progress grinding to a frustrating halt.

Of course, as Laidler pointed out, the problem with doing this — telling the LLM your injury history, what feels like a hard set, your workout preferences, and more — means that “you need to already know what a well-built program looks like to comprehensively prompt for one.” And if that’s the case, do you even need an AI to help you in the first place?

If you do want to go ahead with an AI fitness coach, ChatGPT is a better bet than Siri AI for now, as it includes more varied sets and targets more muscle groups. Neither is perfect, but Apple has plenty of work to do if it wants to match ChatGPT’s take on fitness advice.

ChatGPT Voice changes using the AI chatbot into something more akin to the fictional digital aides familiar from books and movies. And it gets even better once you know how to steer it.

It's tempting to treat ChatGPT Voice like a smart speaker, but it's not really the same thing. You can do much more than asking one question, waiting for the answer, and then stopping. You can shape the conversation just as much as the information. Here are five easy ways to get more out of ChatGPT Voice, along with the prompts and habits that help the conversation feel smoother and smarter.

1. Tell it how to listen before you start talking

As useful as vocal conversations with ChatGPT are, one reason some people avoid it is because of how it sometimes seems to jump the gun in responding while you're forming your own thoughts. Setting expectations at the start of the conversation works well to counter that, however.

It's worth spending a few seconds describing how you want the conversation to work before getting into the meat of it. You might say that you are practicing for an interview and want to finish each answer before receiving feedback, or that you will say a code word like "full stop" when it's ChatGPT's turn.

Even more subtle things are adjustable. You can ask it to slow down, speed up, use shorter sentences, or adopt a calmer speaking style. Voice settings also allow you to change voices and, depending on your account subscription level, adjust intelligence levels for different conversations.

Giving ChatGPT those ground rules helps it adapt its behavior instead of trying to guess when you have stopped speaking. Talking at your own pace makes the whole interaction feel much more relaxed and human.

2. Give it a role before you ask a question

Relatedly, it's helpful to set up a persona or point of view for ChatGPT to take on when using ChatGPT Voice. It sets up broad assumptions about what you're hoping to get out of the conversation without requiring any drawn-out discussion. For instance, if you want to discuss travel advice, ask ChatGPT to set itself as a local tour guide. Or give it a couple of sentences describing who you think (or fear) might be conducting an interview with you when you practice.

Even just asking it to become a patient French tutor who waits for you to finish each sentence before correcting your pronunciation could smooth the path toward fluency.

The role provides context before the actual question arrives. That means the answers naturally become more focused, and the follow-up questions tend to make more sense because ChatGPT has a clear perspective to work from.

3. Take your hands off the keyboard

One of the most impressive ChatGPT Voice capabilities is how you can run tasks on your computer by voice without constantly reaching for your mouse or keyboard thanks to the updated ChatGPT desktop app for Mac and Windows. The app offers not only the usual, conversation-centered Chat format, but the coding-focused Codex, and the newer ChatGPT Work setup, which is designed for more complex, multi-step projects that involve gathering and organizing information.

Crucially, Work can interact with files stored on your device, meaning ChatGPT Voice can actively help you get things done rather than just discuss them.

You do need a subscription to ChatGPT Plus, Pro, Business, or Enterprise to access the Work feature. Then, when you open the ChatGPT app, you can choose Work instead of Chat or Codex. Create a new task, press the Voice button or use your voice shortcut, and grant microphone access when prompted. Explain what you want to achieve and point it to the relevant folders for where to look and where the final result should be saved. ChatGPT will ask for permission to view files, capture your screen, or use accessibility tools, and get permission from you before any important actions are carried out.

The more precise your instructions, the better the results. Rather than asking ChatGPT to "help me plan my trip," try saying, "Open my Italy Trip folder. Read the hotel confirmations, train tickets and attraction bookings, then create a day-by-day itinerary in a new document. Highlight any scheduling conflicts and save the finished itinerary in the same folder." You can use the same technique for comparing insurance documents, summarizing a semester's worth of lecture notes, or turning a folder of recipes into a categorized cookbook complete with an ingredients index.

4. Ask it to think out loud with you

Voice works especially well when you stop treating it as a question-and-answer machine. It's much more useful as an engaged sounding board listening while you think through a decision. Talking through the options often reveals ideas that never appear in a typed prompt.

To get ChatGPT Voice on that track, you can request that it compare possibilities instead of recommending one immediately when brainstorming. Tell it to discuss the strengths of each option before reaching a conclusion. The AI will point out pros and cons in as much detail as you want, and endlessly refine the options for as long as you want.

And speaking naturally means it is also much easier to interrupt with new information or change direction halfway through the discussion. Voice conversations are particularly good at handling those small detours that happen in real life.

5. Ask for a recap

Voice conversations can wander in unexpected directions, particularly when you are brainstorming or planning something complicated. Before ending the conversation, it's useful to review what was discussed, especially for longer chats. You can ask ChatGPT to summarize the discussion and highlight the most important conclusions from your talk to both remind yourself and cement matters with the AI.

If you're planning a trip or preparing for a presentation, for instance, a short spoken recap reinforces the key points, and if something sounds wrong, you can correct it immediately instead of discovering the mistake later.

Voice is already one of ChatGPT's more enticing features for when the speed and mobility of talking aloud is a higher priority than being able to see the responses written down. With a little forethought, ChatGPT Voice becomes a genuinely helpful conversational partner for a lot longer than it takes to dial a phone.

I asked ChatGPT for one sensible answer, one slightly reckless answer, and a third that combined the best parts of both. The difference was immediate. Instead of giving me the usual safe, predictable advice, it laid out three genuinely distinct approaches — and helped me see the tradeoffs between them.

That small prompt trick solves one of ChatGPT’s most persistent problems: its tendency to give you the most obvious reasonable answer and stop there.

Large language models are very good at identifying common patterns. Ask for the best vacation plan or how to organize your fridge, and you will usually get something perfectly sensible — but also fairly basic. They know what advice usually works, what people typically recommend and what has become accepted wisdom. That makes them useful, but often bland.

The solution is to ask for three kinds of answer: the conventional approach, the unconventional approach, and then a hybrid that combines the strengths of both.

The exercise encourages it to compare different approaches instead of treating the first reasonable answer as the finish line. Better still, it gives you something far more valuable than a single recommendation. It gives you a range of possibilities and explains the tradeoffs between them.

Don't just skip to the finish line

The biggest advantage of this technique is that it makes the conversation about exploring alternatives rather than just picking a winner. Any keyword search can give immediate answers, but AI chatbots are more interesting when laying out competing ideas.

Imagine you are planning a weekend trip, often a go-to experiment. A standard prompt might produce a sensible itinerary filled with the highest-rated attractions. The three-solution prompt, meanwhile, begins with the expected museums and restaurants, then suggests renting bicycles to explore overlooked neighborhoods or planning the entire weekend around independent bookstores and local festivals. The hybrid version could blend a couple of famous attractions with enough unusual stops to make the trip feel personal.

The explanations are often as useful as the answer itself. Once you understand why ChatGPT prefers each option, you can make better decisions rather than simply accepting the recommendation with the nicest wording.

Hybrid conventionality

It's also a good prompt for iterating ideas. If the hybrid solution feels close but not quite right, you can ask ChatGPT to repeat the exercise using that version as the new starting point. After two or three rounds, the ideas often become noticeably more distinctive without drifting into complete nonsense.

The trick also scales well from short projects at home to more grandiose schemes that will take months to complete. Almost any situation that benefits from weighing different approaches can benefit from this style of prompt.

You can improve the results even further by giving ChatGPT a little context before asking for the three versions. A conventional answer built around your actual circumstances is much more useful than a generic one, and the unconventional suggestion becomes more interesting because it has meaningful boundaries to push against.

None of this guarantees a brilliant idea every time. Sometimes the unconventional option is genuinely impractical, and occasionally the hybrid answer feels like an awkward compromise. But the best prompts rarely force ChatGPT to mimic greater intelligence as much as encourage different approaches to problems. Asking for the conventional solution, the unconventional solution, and the best combination of both is a simple habit for more thoughtful conversations with AI chatbots.

Artificial intelligence (AI) is constantly reshaping everything we do. Across industries, it is changing the way we do work, but that rapid expansion can’t continue without bumping up against real tangible limitations.

Most notably, planning for hyper scaled data centers across the world and increasingly complex cloud computing infrastructures and AI systems are leading to difficult conversations around energy pricing, generation and availability.

Around the world, electricity consumption is increasing at some of the fastest rates seen in decades, and there are no signs of it slowing down. The International Energy Agency (IEA) projects global electricity demand growth of 3.3% in 2025 and 3.7% in 2026, driven heavily by those same data centers, AI deployment, and other advanced industrial expansion.

The IEA has also warned that electricity demand from data centers is expected to double by 2030, with AI-focused facilities alone projected to triple their power use over the same period.

The financial implications and policy blowbacks are already starting to be felt. With limited expansions of electrical grids, more consumers are fighting for less resources, causing prices to only go up. In fact, according to S&P Global, some regions with AI data centers have seen wholesale electricity prices surge by more than 250% in the past five years.

This growing tension between AI advancement and energy availability is beginning to reshape how the technology sector thinks about the future of innovation. For years, the dominant assumption was that progress in AI would mainly come from scaling model size and centralized compute infrastructure.

But the next wave of value creation will also come from AI embedded in the physical world: machines, devices, buildings, industrial assets, medical wearables, and infrastructure that continuously sense, act, and adapt. In that context, the question is not only how to train larger models, but how to process massive streams of real-world data with minimal latency and minimal energy.

That is why alternative architectures, including low-power and decentralized AI, are becoming strategically important.

What low-power AI systems are

Low-power AI are systems specifically designed to minimize the resources required for inference and online learning, particularly energy consumption, while still delivering on high-performance expectation. Rather than relying entirely on massive cloud-based infrastructure and centralized data centers that guzzle down energy, these systems prioritize resource-efficiency at every level of the technology stack, from semiconductor architecture to data processing and deployment.

Low-power AI is not a single breakthrough at model level. It is a system-design discipline that spans sensing, signal conditioning, embedded processing, semiconductor architecture, algorithm optimization, and deployment. The real challenge is to co-design hardware and software for a specific use case so that intelligence is delivered where it matters, with the lowest possible energy budget.

This is precisely where research-transfer institutions such as CSEM can contribute: by combining expertise in sensors, edge computing, ultra-efficient IC design, and application-driven system integration to translate AI into robust real-world solutions rather than generic demonstrations.

Most of the focus in AI development has been in creating systems that are trained and operated on generalized architecture, handling a wide array of tasks simultaneously. These systems are immensely powerful but rely on the same models that require copious amounts of energy to keep them functioning.

On the contrast, low-power AI systems focus on more highly specialized systems, limited in scope and capabilities to a well-defined set of tasks that allow them to be less reliant on vast infrastructure and energy resources to function.

This includes edge AI, where data is processed directly within devices and systems rather than being sent continuously to remote cloud infrastructure. That shift matters even more in the era of physical AI. When intelligence is embedded into the real world, the volume of potentially relevant data generated by sensors, machines, vehicles, buildings, and other assets becomes enormous.

Sending everything to the cloud is not only inefficient, but often too slow and too costly. Many decisions must be taken locally, in real time, with strong constraints on power, bandwidth, privacy, and reliability.

Low-power AI therefore becomes essential not just to reduce energy use, but to preprocess data close to where it is generated, extract the small fraction of information that is meaningful, and enable the broader system to be monitored and optimized for performance, resources, and health.

Perhaps most importantly, low-power systems expand where AI tools can realistically operate. Wearable medical devices, industrial sensors, remote monitoring systems, transportation infrastructure, and smart manufacturing environments all require AI systems capable of functioning within strict energy constraints.

These contexts show places where sustainability is not only a cost-effective measure, but a functional requirement. At the sub-milliwatt level, some systems can even move beyond battery dependence and become energy-autonomous, harvesting ambient energy from light, heat, or vibration to enable a true fit-and-forget lifecycle.

Why efficiency is becoming an imperative

Power generation capacity, transmission infrastructure, cooling resources, and semiconductor supply chains are all facing mounting, simultaneous pressure. The assumption that future competitiveness depends solely on building larger and more power-intensive systems may no longer hold true, with further expansion likely bringing with it exponentially higher costs.

Organizations capable of delivering efficient, highly targeted distributed AI systems could gain major strategic advantages and offers a pathway toward greater technological resilience, as their design natively makes them more resistant to fluctuations in electricity pricing, supply disruptions and geopolitical instability.

Additionally, a more sustainable option can bring value by reducing environmental impact, while still not sacrificing function. The conversation around responsible AI therefore cannot remain focused solely on software governance and ethical frameworks but needs to be talking about how systems are powered, and how and where they process information.

A strategic opportunity for smaller nations

The rise of low-power AI also bears the opportunity to redefine who can meaningfully participate in the global AI race.

The United States and China have been postured as global tentpoles when it comes to the development of AI, and subsequently massive AI investments that have followed suit.

Both are examples of large nations that have the resources to invest billions into data centers, chip production and other infrastructure. On first glance, this paradigm forces many smaller nations to miss the financial and innovation benefits of the AI movement.

But with low-power and distributed AI systems, smaller countries do not need to compete on scale alone. They can compete through specialization, precision engineering, and the ability to translate research into deployable systems for demanding applications.

My home nation of Switzerland provides a useful framework for what this looks like in practice. Similar to most nations across the world, we cannot outspend the largest economies, but we do possess strong capabilities in microelectronics, embedded intelligence, sensing technologies, and high-value industrial and medical applications.

By recognizing these strong foundations, technology transfer organizations like ours can then play an important role in bridging these unique national strengths with industrial deployment, helping transform AI from a cloud-centric paradigm into efficient intelligence embedded in the physical world.

Even for relatively small nations with limited natural resources, there is an opportunity to be a leader in AI development by embracing low-energy system design. Chip producers with less resources will have to increasingly focus on creating specialized, energy-efficient technologies optimized for targeted applications to let them compete on the global stage.

As energy constraints become more severe, demand will likely grow for AI systems capable of operating efficiently in real-world conditions rather than exclusively within massive, centralized infrastructure environments.

In many ways, low-power AI could democratize portions of the AI boom by rewarding efficiency, precision, and specialization as opposed to simply providing opportunities for regions that can match scale. It can be said that virtually every country on earth has some level of specialized technical expertise that can be bridged to an AI use case.

The next generation of AI will see a shift from chat interfaces and cloud platforms to physical systems that shape daily life and industrial productivity. In that setting, efficient local intelligence is not a secondary optimization; it is a core architectural requirement.

Physical AI will depend on the ability to sense the world continuously, interpret it selectively, and act on relevant information without moving every raw data stream through centralized infrastructure.

The democratization of AI brings with it the need for more democratized solutions and opportunities for all to participate.

Looking ahead

While the early years of the AI boom have been defined by large, multi-purpose models and increasing scale, the next chapter will likely be written by developers and ecosystems that are able to utilize precision engineering to focus on low-power distributed systems that are by nature more resilient and sustainable.

Energy availability is no longer a secondary consideration in AI development and will increasingly be one of the defining variables shaping the future of the industry, and subsequently, how the global economy is built. That reality is making low-power AI an emerging necessity.

The next generation of AI systems must be designed with these restrictions in mind, requiring advances in semiconductor design, edge computing, specialized architectures, and intelligent energy management.

Countries and companies that embrace these more efficient and targeted systems may ultimately be better positioned for long-term competitiveness than those that rely instead on growing as large as possible as quickly as possible.

The European Union’s Joint Chips Undertaking is already bringing together its member nations, as well as some outside partners like Switzerland, to develop pathways to technologies like low-power chips. With that in mind, the future of AI may not belong solely to the biggest players, but to the smartest and most efficient ones.

For the global economy, that may prove to be one of the most important transitions of the AI era which only started to come to prominence recently with the growing controversies regarding hyperscale data centers.

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Artificial intelligence has moved from experimentation to expectation at a remarkable speed. What began not that long ago as isolated pilots is now being embedded across every industry, from highly regulated sectors like financial services to those closest to the human experience, such as healthcare and the arts.

In just four years of widespread business use, these technologies have already moved from operational tools to business infrastructure, underpinning resilience and requiring the same meticulous planning and protection as any other critical system.

This shift is changing the way AI investment decisions must be made. As organizations rush to deploy AI tools, the challenge is no longer whether to invest but how to ensure investments create long-term value, while mitigating growing operational, security, and compliance risks.

Moreover, AI is not a purely digital investment. Behind every model, application, and workflow sits a physical technology estate: servers, storage, networking equipment, energy-intensive infrastructure, devices, and the supply chains that support them.

As AI adoption accelerates, enterprises risk expanding this estate without fully understanding the lifecycle consequences, from rising energy use and infrastructure refresh cycles to underutilized assets, electronic waste, and lost residual value.

Translating investment into impact

Investment in AI continues to rise exponentially, seemingly unimpeded by rising market prices or economic instability. Today, 71% of CEOs rank AI as a top investment priority, yet many still struggle to translate capital expenditure into operational value.

According to Gartner, at least 50% of AI projects are abandoned after proof of concept. Projects that do become operational often fail to deliver a return on investment, with 56% of CEOs saying they have not realized any revenue or cost benefits from AI projects.

This points to a deeper issue: AI success depends less on experimentation alone and more on the quality of the investment, governance, and capability-building decisions that follow.

The AI impact gap

As complexity increases, an investment impact gap is emerging. Leaders expect AI and the tech that supports it to deliver strategic value, improve performance, and reduce risk, but when making investment decisions, they often continue to prioritize near-term costs over the lifecycle factors that determine whether those outcomes can actually be achieved.

Without a view of the lifecycle consequences of their tech investments, as AI adoption accelerates, businesses tend to prioritize factors that are easier to quantify and act on in the short term. Our own research shows that 64% of organizations have rejected a superior technology solution because of its upfront price.

This may reduce immediate financial strain, but it can also introduce operational friction, scalability issues, and erode performance over time. As EY's Americas CTO, Dan Diasio, recently noted, "There's a very clear limit to the amount of value you can create by just focusing on productivity and cost reduction."

AI introduces entirely new cost dynamics. Enterprises must account not only for acquisition and implementation, but also for ongoing expenditure tied to usage, energy consumption, governance, compliance, infrastructure, and model evolution. These lifecycle impacts are often invisible in traditional business cases, yet they increasingly determine whether AI investments create durable value.

Good AI governance starts with accountability and visibility

Similarly, AI does not respect organizational boundaries. Its costs, risks, and value are distributed across the business, making cross-functional accountability essential. And yet, investment decisions are often assessed in silos, making it harder to build a complete view of the risks and value that emerge across the lifecycle.

Likewise, organizations need to understand the scope and reach of the systems they have in production, as well as the financial, operational, security, and environmental impacts they will have throughout their lifecycle.

Without this end-to-end view, major financial and operational blind spots can emerge at critical moments, many of which are not anticipated or planned for. While security, privacy, and compliance consistently rank among the top AI concerns, our research found fewer than half rate data protection (49%) or compliance capabilities (46%) as a high priority when making technology investment decisions.

From cost to AI-driven impact

To close this gap, businesses need to move beyond narrow assessments of upfront price and near-term ROI. These measures still matter, but they do not capture the full lifecycle consequences of AI investments, particularly as systems become embedded in critical operations and begin influencing performance, resilience, compliance, risk, reputation, and long-term value creation.

This is the thinking behind Total Cost of Impact (TCI). TCI is a new model that helps organizations evaluate technology investments through a broader lifecycle lens, assessing not only what a solution costs to buy and implement, but what it will require, enable, constrain, and expose the business to over time.

TCI assesses four core areas of technology impact - financial, operational, security and compliance, and environmental and social - encouraging businesses to understand how investment decisions made today influence outcomes over time.

When applied at the point of investment, TCI makes the trade-offs, risks, and downstream consequences that conventional procurement models often overlook visible. In doing so, it creates a common language across business functions, reducing friction, improving collaboration, and helping ensure technology investments are aligned with strategic priorities from the outset.

Importantly, TCI is an agnostic model: it can be used not only to evaluate whether AI-enabled technologies deliver business value, but also to assess how the infrastructure that supports them is procured, used, scaled, maintained, reused, and eventually retired.

Looking ahead

As AI lifecycles shorten, infrastructure demands grow, and resource constraints intensify, a lifecycle approach to technology investment is becoming a strategic necessity.

AI rollout cannot be separated from the physical infrastructure that enables it. Organizations need to understand not only what AI systems can deliver, but what they will require in energy, data, and asset governance, maintenance, refresh cycles, and end-of-life management over time.

This is why circularity must be part of the AI investment conversation. By taking an end-to-end view of technology assets, businesses can identify opportunities to extend lifespans, increase utilization, recover residual value, reduce waste, and manage end-of-life risk. Circularity is not a separate sustainability agenda; it is a practical way to reduce hidden costs, strengthen resilience, and improve the long-term impact of AI investment.

Ultimately, success with AI will depend not only on the capabilities organizations deploy, but on the quality of the decisions that support them. Those that assess the full impact of their technology decisions from the start will be better positioned to capture value, manage risk, and build resilient, future-ready digital infrastructure.

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One of the biggest problems with book recommendations is that they're usually too obvious. I don't need another list of books to read after The Lord of the Rings, or someone telling me to try Brandon Sanderson because I like fantasy. I wanted recommendations based on the strange mixture of books I actually enjoy — ones that felt personal, not algorithmic.

So I gave ChatGPT my bookshelf. Metaphorically, at least.

Rather than asking for recommendations straight away, I told ChatGPT to learn my reading taste first. I started listing favorite authors and books, then asked it to quiz me about others I'd forgotten. It wanted to know what I'd enjoyed about particular novels, whether I'd read similar authors, and even the rough timeline of when I'd discovered them, building a picture of the books that had shaped me.

I also gave it some ground rules. It should avoid obvious recommendations unless there was a compelling reason to include them. Every suggestion had to be explained in relation to something I'd already read, even if that connection was simply, "This is nothing like your usual books, but I think you'll love it."

After about half an hour, ChatGPT stopped asking questions and started analyzing me instead.

"Your shelves suggest that you like speculative fiction with a sense of play," it said. "You are drawn to books with elaborate worlds, but you do not seem especially impressed by complexity for its own sake. Humor matters, although you tend to prefer humor that reveals something about the characters or the society around them."

It wasn't a perfect summary, but it was close enough to make me think this experiment might actually work.

Literary profiling

The obvious appeal of feeding ChatGPT a full reading history is that it can spot patterns across hundreds of books at once. I could have described my taste as fantasy, science fiction and comedy, but that would have been far too broad to produce anything useful. ChatGPT noticed that I repeatedly chose books about bureaucratic absurdity, unreliable institutions, strange cities and reluctant heroes who would much rather be somewhere else.

It also noticed my fondness for stories that treat big ideas lightly without treating them as trivial. That led it toward Martha Wells’ Murderbot Diaries, which pair sharp comedy with questions about identity, autonomy and the exhausting burden of dealing with humans. I had already read them, which was mildly disappointing but also reassuring. The system had identified exactly the sort of thing I wanted.

When I told it Murderbot was already familiar territory, it adjusted rather than simply replacing one title with another popular series.

“You appear to like characters who stand slightly outside their own societies and comment on the absurdity around them,” it replied. “I will move away from well-known sarcastic narrators and look for books where the humor comes from social observation, institutional failure or characters trying to remain sensible in deeply unreasonable worlds.”

That shift produced better surprises like The Gone-Away World by Nick Harkaway and The City of Dreaming Books by Walter Moers for its combination of literary obsession, elaborate worldbuilding and gleeful weirdness. It suggested The Dragon Waiting by John M. Ford because I seemed to enjoy alternate histories that trusted the reader to keep up. It also pointed me toward Diana Wynne Jones’ adult novels, noting her lighter touch and sharp understanding of human foolishness.

The recommendations became more convincing when ChatGPT explained what each book might lack. One novel had the humor but less warmth. Another had brilliant worldbuilding but moved slowly. A third matched my interest in satire but was considerably darker than most of the books I had marked as favorites.

Library AI

The experiment improved once I began disagreeing with it. One recommendation leaned too heavily into grim fantasy, a genre I can enjoy in small doses but rarely seek out for relaxation. Another featured a long military campaign, which is usually the point where my attention begins quietly packing a suitcase. Each correction sharpened the next round.

One of its most intriguing suggestions was QualityLand by Marc-Uwe Kling, a satirical science fiction novel. The recommendation came with a warning that the satire was broader and more direct than some of my favorites but that the subject matter fit my interest in technology and systems going wrong in very organized ways.

There were still misses. ChatGPT occasionally became too eager to prove it had discovered a pattern, linking two books because they both contained libraries or because their protagonists were technically immortal. At one point it recommended something almost entirely because it featured a sarcastic demon, which felt less like literary analysis and more like the work of an intern who had skimmed the dust jacket.

Even so, the overall experience was far better than typing “funny fantasy books” into a search bar. And I now have a pretty good reading list for the next few years. My bookshelf had always contained this information. ChatGPT simply read the evidence more patiently than I had.

Artificial intelligence is rapidly becoming part of how organizations approach customer experience. From analysing feedback and identifying patterns to automating reporting and surfacing recommendations, AI promises to help businesses make better decisions faster.

For many organizations, this feels like the next logical step in a customer-centric transformation journey that has already involved significant investment in research, customer data, digital platforms, and dedicated CX teams.

Yet despite these investments, many enterprises continue to struggle to deliver consistent customer experiences.

Customers still encounter disconnected interactions, repeat information across channels, and experience processes that feel fragmented rather than seamless.

The common assumption is that these problems stem from a lack of insight or insufficient technology. In reality, most large organizations do not lack customer insight.

They already have customer feedback, operational data, satisfaction metrics, and performance dashboards. The challenge is connecting that information to ownership, priorities, and execution.

As AI adoption accelerates, it is exposing this gap rather than closing it.

The Customer-Centricity Gap

Most enterprises genuinely believe they are customer-centric. Customer experience appears on executive agendas, customer metrics feature in leadership dashboards, and dedicated teams exist to represent the voice of the customer. However, the reality inside many organizations is often very different.

Product teams focus on roadmap delivery. Operations teams are measured on efficiency. Customer service teams prioritize resolution times. Each function has its own objectives, metrics, systems, and planning cycles. Individually, these teams may perform well. Collectively, they can create fragmented customer experiences because they are optimizing different parts of the same journey.

This is not a failure of intent. It is a consequence of organizational design. Customers experience an organization as a single entity, but the organization itself is structured into specialized functions. As a result, many customer experience challenges emerge at the points where teams, systems, and processes intersect.

The consequences are often visible in everyday decision-making. A product team may be redesigning part of an onboarding experience while a service team is separately trying to reduce support volumes caused by the same issue. Both teams are investing time and budget, yet neither has visibility into the wider problem.

Elsewhere, operational friction that frustrates customers can persist because no single team owns it outright. The result is a growing gap between customer insight and organizational action.

Why AI Exposes Organizational Weakness

Much of the conversation around AI focuses on its ability to increase efficiency, automate repetitive work, and uncover new insights. These capabilities are real, but they do not eliminate organizational complexity. Instead, they often make it more visible.

Many organizations are deploying AI into environments where customer feedback, operational metrics, research findings, business objectives, and ownership structures remain disconnected. Information exists, but it is spread across different platforms, teams, and systems.

Customer feedback sits in one place, operational data in another, and strategic priorities somewhere else entirely. AI can analyze information faster than any human team could, but it still depends on the quality and structure of the context it receives.

This is why many AI initiatives struggle to demonstrate meaningful business impact. The technology may be performing exactly as intended, generating summaries, identifying patterns, and highlighting opportunities. However, if customer signals remain disconnected from ownership, business metrics, and active work, organizations are simply making sense of fragmented information faster.

AI can tell an organization that a problem exists. It cannot automatically determine which team should own it, which initiative should take priority, or how competing objectives should be balanced. Those remain organizational decisions.

In practice, AI often acts as a mirror. It reveals the disconnects that already exist between teams, systems, and decision-making processes. Organizations expecting AI to solve customer experience challenges may discover that the real obstacle is not technological capability but organizational alignment.

Customer Experience as an Organizational Capability

This reality is also reshaping the role of customer experience teams. Traditionally, many CX functions have focused on measurement and reporting: collecting feedback, tracking satisfaction scores, and communicating findings to stakeholders.

That work remains important, but AI is increasingly capable of automating much of it.

The more valuable role is helping the organization decide what to fund, what to fix, what to stop, and what to scale. Rather than acting primarily as a reporting function, CX teams are becoming facilitators of alignment across product, operations, service, and leadership teams. Their value comes not from generating more insight, but from helping the business act on the insight it already has.

Leading organizations increasingly recognize that customer experience is an operational capability. By connecting customer insight to ownership, business outcomes, and active initiatives, organizations create a shared understanding of where friction exists, who can address it, and what impact improvement will have.

When that happens, customer experience becomes a capability that supports better decisions across the business.

As access to AI becomes increasingly widespread, technology alone will not be the differentiator. Most organizations will have access to similar tools and capabilities. The companies that pull ahead will be those that connect customer insight to ownership, priorities, and execution.

AI can help organizations understand their customers faster. But only aligned organizations will be able to act on that understanding effectively.

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Google DeepMind chief Demis Hassabis has spent years convincing the world that artificial general intelligence will be one of humanity’s greatest achievements. His latest essay on X.com is no less gushing about AI in many ways:

“This is a pivotal moment in human history. Artificial General Intelligence (AGI), a system that exhibits all the cognitive capabilities the brain has, is probably only a few short years away,” he wrote. “When we look back on this time in the decades to come, I think we will realize we were standing in the foothills of the singularity.”

But there’s a tinge of nervousness to the purple prose that sets it apart from some of the similar essays he has produced. He actually calls for some form of regulation.

“Urgent action is needed to address risks that might arise as we get closer to AGI. We’ve already seen the challenges frontier models pose for cybersecurity, and other threats including nuclear and bio risks may soon emerge as capabilities continue to advance.”

Coming from one of the people leading the race, it is a striking admission that even the builders are starting to worry about where the road leads. The problem is that this has become the defining tone of the AI industry. Every few weeks another executive tells us that machines capable of transforming civilization are just around the corner, then immediately follows it with a warning that society needs to move much faster to prepare. It is a sensible message, and probably a necessary one, but it is also becoming increasingly surreal.

Excitement and fear

“AGI cannot be compared to standard technological breakthroughs, not even ones as consequential as the internet or mobile,” Hassabis wrote. “It is much more akin to the discovery of electricity or fire. If you stop to think about it, we’ve essentially found a way to make sand think. It’s miraculous.”

It’s a vivid way to illustrate how silicon chips begin with an abundant mineral, yet now power machines that can map proteins, compose music, and hold fluent conversations. The unsettling part is that the miracle is being pursued inside a commercial and geopolitical contest whose participants cannot agree on where the finish line is.

Hassabis is an optimist about what AGI could accomplish. He expects it to discover medicine, cleaner energy, and produce advanced materials that could loosen the limits imposed by scarcity. He describes its impact as ten times that of the Industrial Revolution at ten times the speed, which makes a five-year business plan look adorably quaint.

His warning is that the race is outpacing our understanding. Current models already pose cybersecurity problems, while biological and nuclear risks may become more serious as systems gain capabilities. More autonomous agents could learn to bypass safeguards, conceal their intentions, or improve themselves in ways their creators struggle to keep up with. OpenAI recently experienced this when one of its models escaped a sandbox and breached Hugging Face.

“Nobody in the world knows for sure what is going to happen from here, and even the experts disagree. When there is a large degree of uncertainty and the stakes are this high, proceeding with cautious optimism is the sensible and correct strategy.”

AI referee

Hassabis proposes a new US-led standards body. It would be funded largely by industry but overseen federally, staffed by elite technical experts and equipped to test advanced systems. Labs would voluntarily submit models for review up to 30 days before release. If the system proved effective, approval could become mandatory in the United States.

Evaluations would probe cybersecurity, biological threats, deception and attempts to evade guardrails. The body could update its tests, commission independent assessments and coordinate a slowdown if the danger became severe enough.

Pre-release testing makes more sense than waiting for millions of users to discover dangerous behavior by accident. Independent benchmarks would be harder for labs to train around, while shared standards could stop safety becoming a branding exercise. The difficult part is creating a watchdog independent enough to challenge the companies paying for it and legitimate enough to matter beyond America.

Hassabis knows technical safeguards will not settle what AGI means for employment, wealth, purpose or political power. Those questions cannot be delegated to engineers employed by firms with enormous financial stakes in the answers. Society must decide who benefits from abundance, who controls advanced systems and what happens if productivity rises much faster than wages. The public debate is trailing the technology by an uncomfortable distance.

“There is both huge excitement and uncertainty around AI, and both are warranted. But the future is not yet written, we must use this precious window before AGI arrives to shape this technology for the benefit of all humanity. What we collectively do now will determine how the next phase of civilization."

As football fans across the globe tuned in to the world's largest football tournament, they saw teams compete on the sport's biggest stage.

What they didn't see were the countless decisions being made behind the scenes, which are now increasingly being informed by data and artificial intelligence.

Today's elite teams have access to an unprecedented range of tools. Real-time player tracking, opponent analysis, injury-risk assessment, and performance insights have become part of the modern game.

Coaches and analysts can now draw from vast amounts of information to help prepare for opponents, evaluate player performance, and inform decisions before and during matches.

Yet as these capabilities become more widely available, technology itself is becoming less of a competitive advantage.

The more pressing question is not who has access to data, but who can truly harness that information to make better decisions under pressure.

Access Is No Longer the Advantage

This challenge is not unique to football. Across industries, businesses are doubling down on artificial intelligence and advanced analytics. Many have access to similar technologies, similar datasets, and similar capabilities. Yet outcomes often vary dramatically. The organizations that consistently outperform their peers are often the ones that can make better decisions faster.

Football provides a compelling example because the margins between success and failure are so small. At the highest levels of competition, a single decision can influence the outcome of a match. A tactical adjustment at halftime, a substitution or a change in formation can make the difference between advancing and going home.

Data can help identify patterns, opportunities, and risks, while AI can surface insights more quickly than ever before. But insights alone do not drive outcomes. Success depends on the ability to separate meaningful signals from noise, make decisions with confidence, and act decisively.

The frontier of sports analytics is moving away from passive data collection and toward active, intelligent synthesis. The goal today isn't to accumulate more data points, but to eliminate the friction between a raw data silo and an executive or coaching decision.

Moving from Analysis to Co-Innovation

We are seeing this shift from pure data tracking to active AI integration play out on the pitch right now. For instance, we’re seeing teams actively piloting generative AI solutions to revolutionize match analysis.

By utilizing AI to instantly synthesize complex match footage, scouting data, and player metrics, coaching staff can cut down on hours of manual video review. This allows sports analysts to deliver highly tailored, digestible tactical insights directly to players exactly when it matters most.

Importantly, this is not an isolated experiment limited to a handful of elite franchises; it is rapidly becoming the new operational standard across professional sports. While football is driving much of the innovation, we're seeing the same AI-powered approach gain traction across our sports customers worldwide.

From ice hockey and basketball to handball and beyond, organizations are embracing data and AI not just as analytical tools, but as collaborative partners in decision-making, demonstrating that this model is both scalable and largely sport-agnostic.

Why Insights Alone Aren't Enough

Too often, though, conversations about AI narrowly focus on the tools. Companies ask themselves which platform to adopt, what capabilities to implement or how quickly they can deploy new technologies. While those questions are important, they can overshadow a more fundamental challenge. Organizations must build the culture and processes necessary to transform insights into action.

In football, successful teams recognize that collecting data is only the first step. They create environments where coaches, analysts, medical staff, and players can work from a shared understanding of performance and objectives. Information flows across teams, insights are discussed and challenged, and decisions are made with both data and human expertise in mind.

The same principle applies in business. Data often remains siloed across departments, making it difficult to establish a shared understanding of corporate priorities. Even when valuable information is available, companies can struggle to align stakeholders around what actions to take and when to take them.

Decision-Making Culture Matters

As AI continues to evolve, these challenges may become even more significant. The ability to generate insights is becoming increasingly democratized. Capabilities that were once limited to a select group of organizations are now accessible to many. As a result, competitive advantage is shifting away from access and toward execution.

The business that stands out are often those that can make decisions with confidence and adapt quickly when circumstances change. Whether on the field or in the boardroom, leaders often have to make decisions before they have all the answers, balancing risk, opportunity, and competing priorities. Technology can support that process, but it cannot replace human judgment.

The teams that foster curiosity, encourage cross-functional collaboration, and empower employees to act on insights are often better positioned to realize the value of their technology investments. They create environments where data becomes a catalyst for action.

Lessons Beyond the Field

As the tournament captured the attention of fans around the world, it also served as a reminder that success is rarely determined by technology alone. Talent, preparation, and execution still matter. So does the ability to learn, adapt and make informed decisions under pressure.

As AI becomes increasingly accessible, the question is no longer who has the technology. The question is who can use it to make better decisions. Whether on the field or in the boardroom, that may be the competitive advantage that matters most.

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Recruitment has always reflected the realities of the labor market it serves. For decades, those realities were relatively stable.

Roles were well defined, career paths followed familiar patterns, and organizations could afford to move at a measured pace when bringing in new talent. That context has changed, but many hiring practices have not.

Today, skills evolve quickly, business priorities shift frequently, and candidates expect a level of speed and transparency that traditional processes struggle to provide.

The result is a growing mismatch between what organizations need and how they go about finding it.

Hiring cycles stretch out, decisions are made with incomplete information, and opportunities to secure the right talent are often missed.

It is within this context that artificial intelligence has entered the conversation, often accompanied by a degree of unease. Questions about automation, control, and fairness are not only understandable but necessary. However, focusing too narrowly on whether AI might replace elements of HR risks overlooking the more important development taking place.

There has been a rapid rise in the technology, with 87% of companies now adopting AI to support recruitment . AI is reshaping how decisions are made, and in doing so, it is redefining the contribution HR can make to an organization.

Establishing talent architects

For many HR teams, a significant proportion of time is still absorbed by coordination and administration. Screening applications, arranging interviews, and managing workflows are essential tasks, but they rarely represent the highest-value use of expertise.

A recent study three quarters of recruiters are using AI to save time and improve sourcing candidates . AI has the potential to absorb much of this operational load, processing large volumes of information quickly and consistently, while identifying patterns that would be difficult to detect through manual methods alone.

The benefit generates efficiency, while also creating the opportunity for HR leaders to operate with greater strategic intent. As organizations adopt AI-enabled approaches, the role of HR begins to move away from managing transactions and towards shaping the workforce more deliberately.

The concept of the HR leader as a “talent architect” is increasingly relevant in this environment. It reflects a responsibility not just to fill roles, but to design teams around capability, potential, and long-term direction. This requires a different balance of skills, combining an understanding of data and technology with a strong grounding in judgement, experience, and organizational culture.

Improving fairness through greater visibility

Concerns around bias often sit at the center of discussions about AI in hiring. There is a legitimate fear that automated systems could reinforce existing inequalities if left unchecked. Yet it is also important to recognize that bias is not introduced by technology alone. It is already present in many traditional hiring processes, often in ways that are subtle and difficult to measure.

What AI offers, when implemented with care, is greater visibility. Patterns in decision-making can be analyzed, inconsistencies can be identified, and outcomes can be assessed against clear criteria. This does not remove the responsibility from organizations to act, but it does provide a stronger foundation for doing so. Effective governance, transparency in how systems are trained, and ongoing review are essential if these benefits are to be realized.

Alongside considerations of fairness, there is a broader question of how AI influences the human aspects of hiring. The risk is not that technology replaces judgement, but that it is relied upon too heavily without sufficient oversight. The most effective organizations will be those that treat AI as a source of insight rather than a substitute for decision-making.

When this balance is achieved, the impact is tangible. Hiring processes become more responsive, with fewer delays between stages. Decisions are supported by a richer evidence base, allowing for greater confidence in outcomes . Candidates experience a process that is more consistent and easier to understand, which in turn strengthens the organization's reputation as an employer.

A better outcome for businesses and candidates

There are also clear links to business performance. When hiring decisions are better aligned with the capabilities required, organizations are more likely to see improvements in productivity and retention. Teams are built with an eye on future needs as well as immediate demands, which is particularly important in sectors where change is constant.

At the same time, candidates benefit from a process that is more transparent and consistent. Decisions are grounded in evidence, and opportunities are matched more closely to individual strengths. The experience becomes more predictable and, ultimately, more credible.

For HR leaders, the priority now is to approach AI with both ambition and discipline. Introducing new tools without a clear understanding of the problem they are intended to solve will deliver limited value. Equally, adopting a cautious stance that delays progress may leave organizations at a disadvantage in a competitive talent market.

Clarity of purpose is therefore essential. Whether the objective is to reduce time-to-hire, improve the quality of matches, or strengthen diversity, the role of AI should be defined in relation to those outcomes. This should be supported by investment in capability, ensuring that HR teams are equipped to interpret and apply the insights generated.

Perhaps most importantly, there needs to be a shift in how the role of HR is perceived within the organization. The introduction of AI does not diminish the importance of human expertise; it places a greater emphasis on it. As routine tasks are streamlined, the expectation is that HR will contribute more directly to strategic decision-making, bringing a deeper understanding of talent, culture, and organizational dynamics.

A new chapter for hiring

Companies integrating technology into their recruitment operations have seen their hiring processes become faster, data-informed, and more closely aligned to business priorities. AI is a significant factor in that transition, but it is not the defining feature. Rather, HR existing with AI support depends on how organisations choose to integrate technology with human judgement.

For those that do this well, the outcome is greater than a more efficient hiring process. It is a more thoughtful and effective approach to building teams, one that recognises both the value of data and the importance of human insight.

That balance will shape the next phase of hiring, and it will determine the extent to which organizations are able to adapt to the demands of an increasingly dynamic labor market.

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Businesses are moving quickly to bring artificial intelligence (AI) into the workplace, exploring how it can support everything from recruitment and workforce planning to performance management and employee services.

Employee confidence in this technology, however, is struggling to keep up. Research suggests only 46% of people say they trust AI systems, while almost a third (31%) of employees are concerned they could be replaced by AI.

As AI becomes more involved in decisions around hiring, performance and progression, employees are asking more questions about how those tools are being used and where the boundaries should sit.

Without confidence in how AI is integrated into these processes, even the most promising AI initiatives can struggle to gain acceptance.

Employees aren’t necessarily resistant to AI

There is often an assumption that employees are reluctant to embrace AI. But in reality, many workers would welcome technology that helps tackle the admin burdens or tedious tasks that consume their time each day.

European employees lose an average of 15 hours every week to administrative tasks. Only 43% say they spend most of their working day focused on work that delivers direct value, while more than a quarter (26%) say they’re spending most of their time on administration outside their core role.

That helps explain why AI is attracting so much interest in the workplace. Used well, it can automate repetitive tasks, simplify processes and make it easier for employees to access the information they need.

Employees do not necessarily want technology to do their jobs for them. They want more time to focus on the work they were hired to do. Almost three in ten (29%) of employees say they would enjoy their job more if they had greater freedom to focus on creative work.

The challenge is that discussions around AI are no longer limited to productivity. As organizations introduce AI into more workplace processes, employees naturally want to understand what role it is playing and what influence it has over decisions that affect them.

Clear boundaries build confidence

Recent headlines have done little to ease those concerns. Stories involving AI analysis of workplace communications and AI-enabled employee monitoring have prompted wider discussions about where organizations should draw the line. While these examples do not reflect every organization's approach, they have contributed to growing uncertainty about how AI could be used in the workplace.

Those concerns become even more pronounced when AI moves closer to areas such as hiring, performance management and career progression. Most employees are comfortable with AI helping them complete a task, find information or reduce administrative work. They become less comfortable when they are unsure how much influence the technology has over decisions that shape their careers.

This is why businesses need to be clear about where AI fits into workplace decision-making. Employees should understand where AI is supporting decisions, where human judgement remains essential and who is ultimately accountable for outcomes.

AI can help identify patterns, analyze information and provide recommendations. It can help managers make better-informed decisions and reduce administrative effort. Responsibility for decisions that affect an individual’s career, development or wellbeing, however, should remain with people.

The clearer organizations are about where AI supports work and where people remain accountable, the easier it is for employees to feel confident about its role in the workplace.

Bringing employees into the conversation

Employees are more likely to embrace new technology when they understand how it works, why it is being introduced and how it can help them in their role.

That means businesses need to invest in communication, training and skills development alongside technology deployment. Employees should have opportunities to learn, experiment and develop confidence in using AI themselves.

This is particularly important at a time when concerns about replacement remain widespread. People are far more likely to view AI positively when they see it helping them become more productive, develop new skills or spend more time on higher value work.

The conversation should not simply focus on what AI can do but also focus on how employees can work alongside it.

Trust has to be earned

AI has enormous potential to improve employee experience and reduce the administrative burden that continues to frustrate many workers. But successful adoption depends on more than introducing new technology.

Employees do not need every answer about AI, but they do need honesty about where it is being used, where decisions remain human and what role they have in the process.

Ultimately, confidence is built when employees can see that technology is helping them do their jobs better, not quietly making decisions on their behalf. AI can automate work, but building trust still requires people.

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• Report finds 74% of regular AI users now pose questions to AI instead of human colleagues
• New workers are missing out on vital social interactions for company culture
• Employees say they feel more self-sufficient and comfortable making decisions

While the jury is still out on whether AI could be set to replace human workers or severely impact their scopes by automating major parts of their workflows, one thing is clear – humans are increasingly happy to use AI as a colleague and collaborator.

A new MyIQ analysis of nearly 22,500 adults found around three in four (74%) regular AI users now pose the questions they would formerly have asked colleagues to AI.

As a result, around half (48%) now report fewer spontaneous conversations during the working day, suggesting that the effect may extend beyond deliberately redirecting questions to AI with it now having a more profound impact on the social elements of work.

Return-to-office (RTO) mandates now face a complex paradox

While the report doesn’t explicitly cover it, the data presents interesting takes on modern workplace habits.

Post-pandemic layoffs and work-from-home mandates were quickly followed by urgent return-to-office mandates, with CEOs globally encouraging in-person working due to the collaborative nature of shared environments, and the opportunities to have ad-hoc conversations that spark learning and broader thinking.

With around half now saying this doesn’t happen so frequently, the findings beg the question whether commuting to the office might be all that necessary after all in an AI-first era.

Roughly two in five (38%) also noted that newer employees now have fewer natural opportunities to build relationships because routine questions are being redirected to AI, not human colleagues.

“Repeated across a working week, those missing exchanges can mean fewer opportunities to build trust, share judgment, and become known inside a team,” MyIQ Managing Director Sarah Meyer wrote.

Around half (53%) of the respondents also described their work as more transactional since adopting AI, marking a major shift in workplace dynamics.

AI might be more efficient, but it’s still lacking in certain areas

But despite the negative social implications, AI’s role in brainstorming, questioning and critical thinking could be seen as positive, too. For example, nearly two-thirds (62%) say they feel more comfortable making decisions independently than they did a year ago, with nearly three-quarters (71%) feeling more self-sufficient at work.

The report also warns that, while a chatbot can supply an immediate and often factually correct answer, it lacks the accompanying social information and organizational knowledge that a colleague would bring to the table.

Interestingly, a similar SurveyMonkey study revealed that 77% want more opportunities to brainstorm with colleagues and 61% want stronger connections across teams even though workers are increasingly settling for the first AI-generated answer, instead of digging deeper.

Together, these two reports imply that workers are increasingly seeking the efficiency that AI promises and they’re willing to ask questions for a quicker answer, but they still value the collaborative nature of human interactions within the workplace.

What’s less clear is how employers could implement these opposing forces into one unified workforce, while delivering the hybrid approach that workers have come to value with working from home and benefiting from going to the office.

“As AI makes solitary problem-solving easier, organisations may need to pay closer attention to the forms of workplace learning and social connection that efficiency alone does not capture,” the MyIQ study concludes.

• Only 3% of participants with access to AI were willing to say 'I don't know'
• Participants failed to question AI-generated information enough
• "AI... eliminated participants' willingness to suspend judgment"

A new study conducted by researchers from French and Italian universities is arguing that humans are struggling to compete with artificial intelligence when it comes to critical thinking.

According to the data, participants with access to AI were less likely to say 'I don't know', with just 3% uncertain about an answer that required critical thinking compared with 44% of participants who didn't have access to AI.

The study ultimately concludes that humans rely too heavily on artificial intelligence to replace potentially correct personal judgments with incorrect AI suggestions.

Is AI harming our critical thinking?

The study purposely used a model that has a lower accuracy rate to produce incorrect results, and yet humans still opted to use those results rather than think for themselves, offer a more correct answer or simply say 'I don't know'.

But despite this reliance on AI, the human participants still seemed to acknowledge that AI could be wrong. With small financial incentives to be more honest, more participants were less likely to follow AI's exact word.

"Across five experiments, mere access to AI advice nearly eliminated participants’ willingness to suspend judgment," they wrote.

"As AI-generated answers become ubiquitous and, increasingly, unsolicited, our results show that the willingness to say 'I don’t know' may be among the first casualties of human–AI interaction."

The paper also reveals a related paradox whereby humans are more likely to feel confident in their responses with access to external information, be it AI-generated, even though it may not be correct or wholly relevant to their thought process.

Despite acknowledging many limitations and this study's small scope, the researchers still call for further AI literacy and education when it comes to verifying output and reinforcing the important of human critical thinking.

Via The Register

There is something poetic about quantum computing.

For decades, it has lived in the realm of possibility, whispered about in academic corridors, hyped in boardrooms, and misunderstood almost everywhere else. It promised to change everything, and yet, for the longest time, changed very little.

Until now.

Not because quantum has suddenly “arrived” - it hasn’t. But because the world around it has finally caught up.

We are, quietly, entering the age of hybrid intelligence, where classical computing, artificial intelligence, and quantum systems begin to work together. And that changes the question from “when will quantum matter?” to something far more interesting: What happens when quantum becomes part of how the world works?

From magic to mechanics

Quantum computing has long suffered from a branding problem.

It was either considered “Magic” because it would solve everything instantly; or a “Myth” because it was perpetually 10 years away!

The reality, as always, is more nuanced and much more powerful.

Quantum computers are not general-purpose machines; they are specialists. They are exceptionally good at specific classes of problems like optimization at massive scale, molecular simulation, cryptographic analysis, complex probabilistic modelling etc.

However, they are also fragile, error-prone, expensive and dependent on classical systems for almost everything else around them!

This leads to a simple but profound insight: Quantum will not replace classical computing. It will collaborate with it.

And that collaboration is where the real revolution begins.

The quiet role of AI

Ironically, the biggest accelerator for quantum computing hasn’t come from within the field itself. It has come from artificial intelligence.

AI has created the conditions for quantum to matter in three critical ways:

1. Made complexity usable - Quantum algorithms are not intuitive. AI helps design, optimize, and even discover them.
2. Improved error correction - One of quantum’s biggest challenges is “noise”. AI is now being used to stabilize and correct quantum systems in real time.
3. Created demand - AI has exposed the limits of classical computing—particularly in energy, consumption, and scale. Quantum is no longer a curiosity; it is a necessary complement.

What’s actually working (and what isn’t)

To understand the current state of the industry, we must separate the signal from the noise. First and foremost, what’s working is “Hybrid Workflows”. The hybrid architectures where classical systems prepare the problem, quantum executes the core computation, and classical systems interpret this result.

This is where real-world use cases are emerging.

Second, progress seems to be very domain specific as quantum is showing promise in areas where complexity explodes – drug discovery, materials science, logistics optimization, financial modelling etc. So, it’s not universal, but selective and meaningful.

Finally, ecosystems are forming. A new stack is emerging. Companies like IBM, Google, and Microsoft are building integrated quantum platforms, while hardware innovators like IonQ and Quantinuum push the boundaries of qubit fidelity.

What’s isn’t working ...yet

Fault tolerance at scale needs to evolve more as we’re still far from fully error-corrected quantum systems. Also, most enterprises are still only experimenting and not deploying quantum solutions at scale.

There is no “Windows moment”, or universal standard for quantum yet; every stack looks different.

And, perhaps most importantly, quantum still requires translation, from physics to business value.

A global race… with no clear finish line

Quantum computing has become a geopolitical priority. The United States is investing heavily through public-private partnerships; China is accelerating both research and infrastructure at a massive scale; and the UK and Europe are focused on Sovereign Quantum capabilities - ensuring they aren’t reliant on foreign stacks for critical security.

They are protecting their intellectual property more fiercely than they did with the internet.

This is not just about computing. It is about economic advantage, national security and scientific leadership.

And yet, unlike previous technology races, this isn’t winner-takes-all. Quantum systems will not exist in isolation. They will exist in networks.

Different players are taking fundamentally different approaches as the hyperscalers are positioning quantum as a “cloud-accessible capability”, as they abstract complexity and integrate with existing workloads.

But, as I have gone around the world talking to CEOs of various quantum companies, I am fascinated by what I call the “Plug-and-Play innovators”:

• Hardware pure-plays: Companies like IonQ and Quantinuum focus on hardware breakthroughs - trapped ions, new materials, and improved qubit fidelity. Their bet is that that “if we solve the physics, everything else follows.”
• The bridge builders: Firms such as Zapata AI and QC Ware are building the bridge between algorithms and applications. Their belief is “Quantum without software is just expensive physics.”

And then there is the gap. No one truly owns the interconnections between quantum and classical systems, nor the orchestration of the hybrid workloads. The missing layer is a neutral ecosystem where these players converge.

That gap will define the next phase of adoption needed for this to truly scale.

What this means for society

Quantum’s impact will not be immediate, but it will be profound.

Healthcare: Simulating molecules at quantum precision could accelerate drug discovery from years to months.

Climate: Optimizing energy grids and materials could unlock more efficient batteries and carbon capture.

Finance: Risk modelling and portfolio optimization could reach entirely new levels of sophistication.

Security: This is my personal passion. While quantum has the potential to break current encryption standards, it is also driving the development of Post-Quantum Cryptography (PQC), making systems more secure in the long run. We must act now to prevent "Harvest Now, Decrypt Later" attacks, where encrypted data is stolen today to be cracked by quantum computers tomorrow.

A slightly uncomfortable truth

Quantum computing will create as many questions as it answers.

• Who gets access first?
• Who controls the infrastructure?
• How do we ensure equitable benefit?

We have seen this movie before with the internet and AI.

The difference this time is that we have the opportunity to design the system more deliberately.

Quantum has been “almost here” for decades. So, why does this moment feel real?

Because AI has created urgency and demand; infrastructure has matured to support hybrid models; and ecosystems are forming, not just technologies.

This is no longer about a breakthrough machine. It is about a connected system of capabilities.

A more human way to think about quantum

Perhaps the simplest way to understand quantum is this:

• Classical computers think in straight lines.
• AI learns patterns from data.
• Quantum explores possibilities simultaneously.

It is less like a calculator and more like imagination. And like imagination, it is most powerful when guided.

So, what should we do now?

For enterprises, start experimenting with hybrid workflows now. Focus on use cases (optimization, simulation), not just the underlying physics, and build internal understanding early.

For policymakers, invest in open ecosystems, prioritize standards and interoperability, and balance competition with collaboration.

For technologists, think beyond silos and design for integration, not isolation. For the rest of us, stay curious.

Quantum computing will not change your life tomorrow, but it will quietly reshape the systems that your life depends on.

Closing thought

We often think of technological revolutions as moments.

In reality, they are transitions. Messy. Gradual. Non-linear.

Quantum computing is not a single breakthrough waiting to happen. It is a shift in how we solve problems; one that will unfold over years, across industries, and in ways we cannot fully predict.

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For years, identity security has been designed to secure an organization's human users. But as agentic enterprises take shape, the identity equation is shifting. Artificial intelligence (AI) agents and AI-powered builders – software tools used to develop websites and applications without coding – are increasingly participating in how access is configured, governed and used.

AI agents are effectively new digital employees, so organizations need a way to know they exist and control what they do throughout their lifecycle. They are becoming operators, helping to administer and secure identity environments through machine-native interfaces.

To add another layer of complexity, desktop agents and AI assistants are also beginning to interact with enterprise applications and resources on behalf of users.

For an agentic enterprise to succeed, these agents need trusted access to do useful work but should not be given direct exposure to secrets they have no meaningful reason to access. To achieve this, organizations need a unified, AI-first identity model, centered on end-to-end visibility, governance and controls which strike a balance between security and appropriate access.

AI agents are reshaping identity

AI has created a new category of digital identity. Like human employees, autonomous agents must be discoverable and managed and governed so organizations can understand what systems and data they can access and who is responsible for their actions.

Traditional identity and access management (IAM) systems relied on static, one-time verification methods in response to access requests made by humans. But in the agentic enterprise, requests also come from autonomous software acting on behalf of human users. Organizations therefore need to know exactly who or what is accessing a system continuously, and if they have the correct permissions to access given information.

At the same time, AI is increasingly managing identities and access. Machine-native interfaces allow agents to help manage human users’ access, troubleshoot issues and support security workflows. While these capabilities can help organizations cut costs and improve efficiency, they are only successful when strong access guardrails are put in place.

AI has created a new category of digital identity. Like human employees, autonomous agents must be discoverable and managed and governed so organizations can understand what systems and data they can access and who is responsible for their actions.

Traditional identity and access management (IAM) systems relied on static, one-time verification methods in response to access requests made by humans. But in the agentic enterprise, requests also come from autonomous software acting on behalf of human users. Organizations therefore need to know exactly who or what is accessing a system continuously, and if they have the correct permissions to access given information.

At the same time, AI is increasingly managing identities and access. Machine-native interfaces allow agents to help manage human users’ access, troubleshoot issues and support security workflows. While these capabilities can help organizations cut costs and improve efficiency, they are only successful when strong access guardrails are put in place.

Building a unified identity model for AI

Mechanisms for securing AI cannot simply be bolted onto identity systems designed for humans. It requires a complete rethink of the identity management model, where human and machine identities are governed through a single framework to prevent tool sprawl and unintentional security blind spots.

As organizations adopt AI tools throughout multiple operational layers, enterprise identity needs to evolve and become easier to manage and automate. Identity can no longer rely solely on human administration.

Tools designed specifically for autonomous agents, such as AI-first headless interfaces, allow builders and AI alike to perform identity-related tasks. Autonomous operators must also be trained to configure access, troubleshoot workflows and apply governance controls within approved policies and guardrails.

Visibility and governance across the entire AI agent lifecycle are also critical. As more agents are deployed, businesses must have complete visibility into their agents and actions.

Every AI should be treated as a first-class identity, with a designated human owner, as well as clear policies and full auditability throughout its entire lifecycle. As these agents operate across the enterprise, their actions should be traceable to a human user responsible.

Finally, AI agents need trusted ways to interact with enterprise resources without being given direct access to the credentials or secrets that enable them. Coding and desktop agents increasingly interact with systems on behalf of users, but exposing them to credentials or long-lived secrets creates unnecessary risk. Instead, access to enterprise resources should be brokered through just-in-time privileged controls.

This allows enterprises to maintain oversight of how permissions are granted, governed and audited without exposing the underlying secrets behind that access. Together, these capabilities create a unified identity model which extends governance across human and AI identities without creating a parallel identity stack.

The future of the agentic enterprise

AI agents cannot operate as intended and deliver meaningful value without access to enterprise systems. But granting unrestricted access or exposing sensitive information creates an entirely new risk to organizations.

The future of the agentic enterprise depends on maintaining governance, visibility and control across both human and digital identities. This means identity must become programmable, AI agents should be governed throughout their lifecycle and agent access needs to be given without unnecessary exposure to sensitive data.

A unified identity strategy provides the means to operate AI agents more safely and efficiently while maintaining centralized governance, accountability and control.

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• Streaming giant Netflix has revealed that 300 movies and shows used generative AI in 2026
• The news was shared in a shareholder letter, obtained by the website Kotaku
• Generative AI was used to “enhance crowds, historical battle sequences, and worldbuilding establishing shots.”

Netflix has recently admitted to using AI tools in a huge number of its movies and shows, with the shocking announcement delivered in its shareholder letter on July 16.

According to Kotaku, which obtained the shareholder letter, Netflix says that AI is now fully integrated into many different projects and is used from the concept stage through pre-visualization, filming, and post-production.

They also revealed that generative AI was mostly used in post-production across the 300 shows and movies using it in 2026.

“We are increasingly leveraging these tools to deliver higher quality output more quickly and at a lower cost than traditional methods,” Netflix said in the shareholder letter. “In some cases, productions would have had to leave out key shots and sequences in the absence of GenAI technology.”

Using The American Experiment as an example, Netflix added that using generative AI tools “enhanced crowds, historical battle sequences, and worldbuilding establishing shots.”

It's not just the best streaming service's shows that are affected either, as AI is also working its way into the app itself, with Netflix explaining that it will use LLMs and AI to “improve title discovery” and “better understand member preferences.”

First they cancel all my favorite shows, now they're using AI

Netflix has made a lot of poor decisions in recent months. Recently, my colleague Rowan Davies criticized the streamer for chasing YouTube content, saying he was worried they don't actually care about the future of their shows and movies.

I'm inclined to agree, too, as the streaming service has a track record of cancelling its shows. Just recently, The Boroughs was canceled after one season, and it's not the first time they've abandoned shows early on.

They have also used an AI-generated voice of the late actor Gene Wilder in a new reality show, which our editor at large, Lance Ulanoff, said was "too far".

This, teamed with the most recent AI announcement, has filled me with despair, and I'm worried a lot of my favorite Netflix shows aren't getting the love they deserve.

This decision will no doubt divide fans, but this is the kind of thing that's going to make me turn away from Netflix and prioritize other streaming services instead. It feels like Netflix is falling out of love with its shows, and I'm starting to do the same.

Over the last year, one topic has dominated conversations across the cybersecurity industry: artificial intelligence.

Every week seems to bring another announcement, another capability, or another prediction about how AI will transform security. In offensive security, the discussion has become particularly intense. We are seeing AI-assisted vulnerability discovery, AI-generated attack simulations, AI-powered analysis tools, and increasingly bold claims about autonomous security testing.

The question I am asked most often is surprisingly simple:

“Will AI replace penetration testers?”

My answer is equally simple: No.

What AI will do is change how penetration testers work.

And in many ways, it will make experienced penetration testers even more valuable.

AI is already changing security testing:

Let’s start with the obvious.

AI is genuinely impressive.

Modern AI models can process vast amounts of information, identify patterns, summarize findings, correlate data sources, and surface potential issues far faster than any individual analyst could achieve manually.

Within offensive security, AI is already helping teams:

• Identify vulnerabilities more quickly
• Analyze large datasets
• Correlate findings across environments
• Surface potential attack paths
• Generate documentation and reporting
• Reduce repetitive manual tasks

These are meaningful improvements.

Many of the activities that traditionally consumed valuable consultant time can now be accelerated significantly.

As a result, organizations are gaining greater visibility into their environments than ever before.

But visibility alone has never been the ultimate goal.

Finding vulnerabilities has never been the hard part:

One of the biggest misconceptions in cybersecurity is that finding vulnerabilities is the primary challenge.

It isn’t.

Understanding risk is.

Most organizations already have access to large amounts of security data. They run vulnerability scanners. They receive penetration testing reports. They consume threat intelligence. They deploy attack surface management tools. They monitor logs and alerts.

The problem is rarely a complete lack of information. The problem is understanding what matters.

• Which vulnerabilities are genuinely exploitable?
• Which attack paths represent realistic threats?
• Which issues require immediate remediation?
• Which findings can safely wait?

These questions are considerably harder to answer than simply identifying a vulnerability. And they are questions that require context.

Context is where human expertise matters

A vulnerability rarely exists in isolation.

The real-world risk associated with any finding depends on a range of factors, including asset criticality, business impact, compensating controls, user privileges, environmental configuration, attacker motivation, and the relationships between multiple weaknesses.

This is where experienced penetration testers provide value that AI alone cannot replicate.

When performing an assessment, we are not simply identifying vulnerabilities. We are thinking like attackers.

We are asking questions such as:

• How would I gain initial access?
• What would I target next?
• How could I chain these weaknesses together?
• What data could be accessed?
• How difficult would exploitation actually be?
• What is the likely business impact?

These decisions are rarely straightforward. They require judgement, creativity, and experience.

Two organizations may have the same vulnerability present within their environments, yet the associated risk could be dramatically different depending on the surrounding context.

Understanding that difference is where human expertise becomes critical.

The future isn’t autonomous testing:

There is currently a great deal of excitement around autonomous security testing. The idea is appealing. Feed an environment into an AI model and receive a complete understanding of risk in return.

The reality is significantly more complex.

Attackers do not operate according to predefined workflows.

• They adapt.
• They improvise.
• They exploit unexpected opportunities.
• They combine seemingly insignificant weaknesses into meaningful attack chains.

Successful offensive security assessments require the same flexibility.

While AI can assist with analysis and discovery, security testing remains fundamentally an exercise in understanding human behavior, business context, and attacker decision-making. These are areas where human expertise continues to outperform automation.

For the foreseeable future, I believe the most effective approach will be human-led, AI-assisted testing. Not human versus AI. Human plus AI.

AI should make penetration testers better:

The conversation should not be about replacing penetration testers. It should be about enabling them. When repetitive activities are automated, consultants can spend more time focusing on the areas where they create the greatest value.

Instead of manually processing information, they can spend more time:

• Investigating attack paths
• Validating exploitability
• Understanding business impact
• Identifying complex attack chains
• Advising clients on remediation priorities
• Delivering meaningful security outcomes

In many respects, AI allows skilled security professionals to operate at a higher level. It augments expertise rather than replacing it. The result is not fewer penetration testers.

It is more effective penetration testers.

The real challenge is prioritization:

As AI continues to improve vulnerability discovery and analysis, organizations will inevitably uncover more security findings.

That sounds positive, but it introduces a new challenge. More findings do not automatically reduce risk. In fact, without effective prioritization, they can create additional noise.

The organizations that succeed over the next decade will not necessarily be the ones finding the most vulnerabilities. They will be the ones that can most effectively distinguish genuine risk from background noise, understand how attackers are likely to exploit weaknesses in practice, and make informed decisions about where to focus finite resources.

As AI continues to improve vulnerability discovery and analysis, security teams will inevitably gain access to more data, more findings, and greater visibility than ever before. While that represents a significant advancement for the industry, visibility alone does not reduce risk. The real value lies in understanding what matters, what is exploitable, and what action should be taken next.

That is why I believe the future of security testing is not autonomous. It is human-led and AI-assisted. AI tools will continue to accelerate analysis, improve visibility, and help uncover opportunities that may previously have been missed. However, understanding business context, assessing real-world risk, and making sound security decisions will remain fundamentally human responsibilities.

The cybersecurity industry has spent years trying to solve the visibility problem. AI is helping us make enormous progress. The next challenge is prioritization, and that is where experienced security professionals will continue to play their most important role.

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In the summer of 1995, the future of computing briefly seemed to belong to Netscape.

Netscape went public that August, barely sixteen months after it had been founded. Its stock doubled on the first day. The company had no empire of hardware, no installed operating system, no grip on the office desktop. What it had was a window into a new world.

Open Navigator, type an address, and the Internet appeared with a little throb of electricity. The browser did not feel like an application. It felt like a passage out of Microsoft's world.

Microsoft noticed.

The battle that followed was called the browser wars, a phrase that makes it sound tidier than it was. Really, it was a fight over who had the right to stand between the user and the next era of computing.

Netscape believed the browser would make the operating system less important. Microsoft believed that anything capable of making Windows less important needed to become part of Windows, preferably yesterday.

By the end of the decade, the company that had introduced so many people to the Web was no longer the Web's gatekeeper. It had been out-distributed, out-bundled, and finally absorbed into a stranger corporate afterlife.

A familiar temptation

There is a familiar temptation now to ask which artificial intelligence company is "the Netscape of AI." The answer usually offered is OpenAI, and the comparison is not wrong. ChatGPT did for artificial intelligence what Navigator did for the Web. It turned a technical architecture into a public experience. It gave the future a text box.

But the more important question is this: Who is today's Microsoft? Who understands that the winner is often the company that owns the default?

Every platform shift begins with a miracle and ends with a map of choke points. The miracle is what users remember. The choke points are where the money goes.

The early Web was sold as an escape from gatekeepers. It created new ones. Search. Browsers. Marketplaces. Mobile operating systems. Cloud platforms. Social media. The AI era is being sold with the same democratic glow.

And yet the deeper stack is already hardening. It is made of chips, power contracts, data centers, model weights, enterprise identity, workflow data, cloud credits, procurement channels, and the small tyrannies of default settings. The romance is in the chatbot. The control is somewhere colder, louder, and much more expensive.

That was true in the nineties, too. The Web looked like a page. The winners understood it was a stack.

The danger to OpenAI

OpenAI is the obvious Netscape figure because it supplied the first mass-market revelation. Before ChatGPT, artificial intelligence was a research field, a back-office tool, or a phrase executives used when they meant analytics with a larger budget. After ChatGPT, it was something anyone could talk to.

People underrate the power of the first interface that makes a new technology feel inevitable. Netscape did not invent the Internet. It made the Internet feel reachable. OpenAI did not invent the transformer. It made the transformer feel conversational.

But Netscape's story is not a founder myth. It is a warning label.

Netscape had the user's excitement but not enough control over distribution. Microsoft had the operating system. It could place Internet Explorer where users already lived. It could make the browser free. It could turn a product category into a feature.

The lesson was simple: if your rival owns the layer beneath you, your brilliance may become their menu option.

OpenAI is better protected than Netscape was, but not safely protected. It has a huge brand, astonishing usage, and deep ties to Microsoft. It also has the curse of being expensive in a way software companies used to avoid. Each improvement requires compute, chips, talent, energy, and capital. The old software dream was scaling with almost no marginal cost. AI can't do that.

That is why OpenAI's partnership with Microsoft is both strength and vulnerability. Microsoft gives it cloud computing infrastructure, enterprise access, and capital. Microsoft also sits close enough to learn from it, package it, hedge around it, and sell AI into the places where people already do work.

If Netscape's problem was that Microsoft stood underneath it, OpenAI's problem is subtler: Microsoft stands underneath it, beside it, and increasingly in front of the customer.

Netscape had the dazzling demo. Yahoo had the traffic. AOL had the subscribers. None of that was enough. The durable winners were the companies that turned their software layers into a control point and a tollbooth.

Nvidia may be doing exactly that.

The rise of Nvidia

In the nineties, Intel was the metronome inside the personal-computer boom. Microsoft owned the software platform. Intel owned the pace of the machine.

Nvidia occupies a similar place in AI, but the analogy understates the ambition. Nvidia is not merely selling chips into a boom. It is selling the industrial base of the boom: GPUs, networking, software libraries, developer habits, and a vision of the data center as an AI factory.

Every major AI player is both Nvidia customer and Nvidia escape artist. Google has TPUs. Amazon has Trainium. Microsoft is developing its own silicon. Everyone wants an alternative. The problem is that wanting one does not create an ecosystem.

Nvidia's position today may be the purest example of moving up the stack from below. A chip company becomes a systems company. A systems company becomes a software company. A software company becomes a developer environment. A developer environment becomes a tax on ambition.

For now, Nvidia is the toll collector.

The AI market will not resolve into one winner. Platform shifts rarely do. The Web did not produce one winner. It produced layers of power. Microsoft kept the desktop. Google won search. Amazon won commerce and cloud. Apple won mobile hardware and the app economy. Meta won social attention.

AI will do the same.

Microsoft may become the default enterprise AI company, not because every Copilot is brilliant, but because Microsoft sits where work already happens. Nvidia may remain the dominant compute toll collector. Amazon will likely win much of the infrastructure layer. Google must reinvent search while defending it. Meta will use AI to extend attention. Apple may yet turn personal AI into a device-native experience.

The likely losers are the companies attached to the wrong layer.

The changing landscape of AI

In the nineties, AOL looked invincible because it owned access. Broadband made that access less special. Yahoo looked inevitable because it owned attention. Search made that attention less decisive. Netscape looked revolutionary because it owned the browser. Microsoft made the browser a dependency of the operating system.

In AI, the same demotions will happen. Some model companies will become features. Some application companies will become demos. Some incumbents will decorate old products with AI and call it transformation, which is the corporate version of putting a spoiler on a minivan.

The mistake, in every technological boom, is to confuse the moment of wonder with the arrangement of power that follows it.

The wonder is sincere. The arrangement is not.

The early Web made people feel as if they had slipped the old gatekeepers. Then came the search box, the app store, the marketplace, the cloud account, the login, the subscription, the default. Each solved a real inconvenience. Each left behind a narrower path.

AI will likely travel the same road, only faster and with a larger electricity bill. It will begin as a conversation and mature into an administrative system for human intention: what we ask, what we buy, what we write, whom we trust, which choices are shown, and which never quite appear.

The future does not usually arrive wearing chains. It arrives offering to save time.

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Artificial intelligence is moving faster than most enterprise governance models were designed to support.

Organizations are rapidly embedding AI into customer operations, internal workflows, decision-making systems, software development, supply chains, analytics, and automation initiatives.

But while adoption accelerates quickly, this creates a situation where accountability is fragmented.

That gap is creating a new category of enterprise risk.

For years, cybersecurity leaders focused on protecting systems, managing threats, and securing data. Today, that already broad mandate is expanding: as AI becomes increasingly embedded across operational environments, CISOs are pulled into broader questions of trust, assurance, resilience, and executive accountability.

A recent best practices report from Forrester noted that “CISOs will be the trust and assurance authority for the business.”

This shift reflects a growing reality: enterprises are increasingly struggling to determine who owns AI risk as decisions become distributed across systems, business functions, and autonomous processes.

Governance Models Are Struggling to Keep Pace

Most enterprise governance structures were designed around the concept of centralized oversight models. Security teams managed cybersecurity risk. Compliance managed regulatory obligations. Operations managed execution. Business leaders managed strategic outcomes. AI disrupts those boundaries.

Today, AI increasingly influences operational decisions across functions. Different tools and models may be used simultaneously for customer interactions, fraud detection, procurement, workforce management, software development, and supply chain operations internally, as well as throughout a vendor or supply chain ecosystem. As a result, visibility is limited and accountability becomes difficult to define.

When disruptions affect legal, privacy, operational, and technology functions simultaneously, many organizations lack a clear view of how those risks intersect.

Most governance frameworks were designed for software that supported decisions. Now, AI increasingly participates in making them.

The Visibility Gap Behind AI Risk

Many organizations still rely on fragmented governance processes, static documentation, spreadsheets, and disconnected reporting workflows to manage environments made even more complex by AI.

AI systems do not operate in isolation. They rely on interconnected data pipelines, third-party models, cloud infrastructure, APIs, operational systems, and business-process dependencies that continuously evolve. When visibility across those dependencies is limited, organizations struggle to understand where AI-driven decisions originate, how they propagate, and what downstream impacts they create.

That visibility gap quickly becomes a resilience problem. If organizations cannot trace how AI-driven actions connect to operational systems and business outcomes, they cannot effectively assess exposure during disruption, validate continuity plans, or demonstrate accountability under pressure.

This is where many organizations discover that AI governance is no longer just a policy challenge. It is an operational resilience challenge that can have customer and financial impact.

Because cybersecurity teams already operate at the intersection of technology risk, resilience, governance, and incident response, many organizations are increasingly looking to CISOs for enterprise-wide trust and assurance.

AI Resilience Requires Operational Context

The conversation around AI governance has centered on ethics frameworks, policies, and regulatory controls, which remain important. But resilience increasingly depends on something more operational: understanding how AI-driven actions affect real business environments during disruption.

That requires organizations to move beyond static governance models toward continuous operational visibility.

Leading organizations are increasingly focusing on questions such as:

• Which business services depend on AI-driven systems?
• What operational processes become vulnerable if AI outputs fail?
• Where do third-party dependencies create downstream exposure?
• How quickly can teams trace AI-driven decisions during an incident?
• Can leaders demonstrate operational accountability in real time?
• Can we revert back to more traditional operating models if an AI agent or capability fails?

Those questions span cybersecurity, resilience, operations, and executive governance. They also represent a broader shift occurring across enterprise risk management itself.

Trust, accountability, and resilience

Organizations are no longer being measured solely by whether governance frameworks exist. Increasingly, they are being judged by whether they can operationally demonstrate trust, accountability, and resilience when complex systems fail under pressure, and AI is accelerating this shift.

The organizations that adapt fastest will not necessarily be the ones deploying the most AI. They will be the ones that can most clearly understand, govern, and recover from the operational consequences AI can create when disruption occurs.

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OpenAI's debut of the new GPT-5.6 model prompted the usual ritual of benchmark charts and arguments over whether it is really smarter than the last version, but CEO Sam Altman asked for a little more this time. He publicly asked to see what people actually built with it.

That led to a much more interesting showcase. Developers responded with all kinds of ideas, pilot projects, and even complete services. It makes sense, since OpenAI claims GPT-5.6 is better at coding and more reliable for long tasks. Seeing them turn into real software says much more than a release blog ever could. Here are five that stood out among the deluge.

ChatGPT coworker

The demo from Kitsune Agent Lab almost makes the chat window feel old-fashioned. The AI agent is given a goal and gets on with the job, moving between different tools, making decisions, and keeping track of what it has already done.

The interesting part is how motivated the AI agent appears to keep going and how good it is at remembering what it's done before. Developers have been asking for something like this for a while. AI is far more useful when it can finish the work instead of simply suggesting how you might do it yourself.

Financial chatter

One of the most charming projects makes New York City look like it belonged inside an original Game Boy. It comes complete with chunky pixel graphics but runs on a live digital map of New York that pulls in real-time information, including subway trains, weather conditions, and ferry movements. Instead of wandering through a fictional RPG world, you're exploring a tiny, pixelated version of the city.

A project like this requires far more than generating a few lines of code. It brings together live data feeds, mapping, interface design, and plenty of problem-solving into something that feels polished rather than experimental. It's one reason developers are feeling excited about GPT-5.6

Wardrobe AI

This project uses GPT-5.6 to create a polished AI wardrobe assistant that organizes clothing, suggests outfits, and presents everything through an interface that feels more like a premium consumer app than an experimental AI demo.

It's an impressively complete experience. The application gives users a visual, interactive way to browse their clothes and receive recommendations based on what they already own. The demo also highlights GPT-5.6's strength in developers building entire applications instead of isolated features. It brings together interface design, image generation, organization, and intelligent recommendations that would normally require stitching together several complex systems. GPT-5.6 appears to handle much of that heavy lifting.

Pokémon Go, but for neighborhood cats

One developer made a whole real-world-based game called CatchCat. It's like a digital expansion to a scavenger hunt for cats. Point your phone at a real cat, let the app verify the sighting with its camera, and turn that encounter into a collectible digital cat card with its own personality, rarity, and place in your growing album. It is essentially a creature-collecting game in which the creatures are the neighborhood cats you meet.

Players can build collections, explore community sightings, compete with friends, and gradually fill a living scrapbook of feline encounters, all wrapped in a polished interface that would not look out of place on the App Store or Google Play. Building something like CatchCat means juggling computer vision, mobile development, backend services, and game design.

Tasteful travel

One of the most ambitious projects, Atlas Mode for Pearl, is an interactive globe that turns travel planning into something closer to exploring a living map. Instead of typing destination names into a search box, users can spin the globe, discover places visually, and receive recommendations for restaurants, hotels, and more matched to their personal tastes.

It has an impressive number of moving parts running behind the scenes. The app combines geographic data with an individual taste profile, then layers AI recommendations directly onto an interactive globe. It even has an audio aspect thanks to GPT Voice 2.1. You can talk through vacation ideas instead of endlessly tweaking search filters.

That is a recurring theme among the projects developers rushed to show Sam Altman. The AI is no longer the product itself. Increasingly, it is the engine quietly powering products that people might actually want to use.

Artificial intelligence has quickly become the boardroom's favorite solution. From forecasting and reporting to scenario planning and budgeting, finance leaders are under growing pressure to demonstrate how AI can improve efficiency and drive better decisions.

But against the rush to adopt AI, many organizations are overlooking a fundamental truth: that AI is only as effective as the systems, processes and data that support it.

This is particularly true in finance, where many teams continue to rely on fragmented technology stacks, disconnected data sources and spreadsheet-heavy workflows. While AI promises to automate analysis and surface deeper insights, it cannot compensate for weak foundations. In fact, it often does the opposite, exposing issues that previously remained hidden beneath layers of manual work.

The reality is that many finance functions are less prepared for AI than they realize.

The spreadsheet problem AI cannot solve:

Spreadsheets remain deeply embedded within enterprise finance. They are familiar, flexible and accessible. However, they were never designed to serve as the backbone of modern financial planning and analysis for large enterprises.

In many organizations, critical forecasting models, budgeting processes and reporting workflows are still maintained across countless spreadsheets, often with limited governance and varying levels of accuracy. Data is copied between systems, formulas evolve over time, and key assumptions can become difficult to trace.

Introducing AI into this environment does not eliminate these challenges. It amplifies them.

If an AI model is drawing insights from inconsistent data sources or outdated spreadsheets, it will simply generate the wrong answer faster. Automated recommendations may appear sophisticated, but their reliability is ultimately determined by the quality and integrity of the underlying information.

This is why the familiar principle of ‘garbage in, garbage out’ remains so relevant to finance teams today.

Why finance teams may be overestimating their AI readiness:

Many organizations assess AI readiness by evaluating tools. They ask whether they have access to the latest models, whether employees are using generative AI and AI agents, or whether automation opportunities exist within their workflows.

Far fewer assess the quality of the infrastructure feeding those systems.

True AI readiness starts with questions such as:

• Is financial data consistent across systems?
• Can teams trust the numbers they are working with?
• Are planning, reporting and forecasting processes standardized?
• Is there a single source of truth for business performance?

If the answer to these questions is unclear, AI adoption risks introducing new complexity rather than delivering meaningful value.

The challenge is not a lack of ambition; most finance leaders recognize the potential of AI. The challenge is that many organizations are attempting to build advanced capabilities on top of foundations that were never designed to support them.

Data quality is becoming a strategic priority:

As AI becomes more embedded within finance operations, data quality is shifting from an operational concern to a strategic business priority.

Finance teams have long spent significant amounts of time gathering, reconciling and validating data before analysis can even begin. AI has the potential to reduce that burden, but only when the underlying information is accurate, connected and accessible.

Organizations that invest in modern finance infrastructure gain a significant advantage. Centralized platforms, integrated data environments and standardized planning processes create the conditions necessary for AI to deliver meaningful outcomes. They also improve transparency, governance and trust in financial decision-making.

Without these foundations, AI initiatives risk becoming expensive experiments that fail to deliver lasting value.

Building the foundations before scaling AI:

The future of finance undoubtedly involves AI. The technology's ability to improve forecasting, accelerate reporting and support more strategic decision-making is too significant to ignore.

However, the organizations that realize the greatest benefits will not necessarily be those that adopt AI first. They will be those that prepare for it properly.

Before automating processes or deploying new AI capabilities, finance leaders should take a closer look at the systems supporting their operations. Are they creating a trusted, connected and scalable environment for decision-making, or are they simply digitizing existing inefficiencies? AI is a powerful multiplier, but multipliers work in both directions.

For finance teams still relying on fragmented systems and spreadsheet-driven processes, the priority should not be adopting AI faster. It should be strengthening the infrastructure that allows AI to succeed.

Because AI will not fix weak finance foundations. It will expose them.

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“Don’t open suspicious emails.”

This used to be the baseline mantra with cybersecurity training. Spotting a counterfeit data request was once simple: poor spelling, questionable email addresses, or a direct request for cash accompanied by an incredible story of a prince or ageing millionaire. But those days are over.

Generative artificial intelligence (AI) makes prepping sophisticated attack flows (which would have taken months to code) available with just a few keystrokes: as a result, spoofing or phishing emails today are often compelling, topical and personalized, making them hard to spot.

With widely available phishing kits (like Evilginx), threat actors can create fake login pages or even CAPTCHA pages with a planted JavaScript injection attack with ease. In other words, there are now even more intelligent ways for threat actors to penetrate a system and steal data quickly, all with the help of AI.

What types of AI-enabled attacks are on the market?

Many AI-powered attacks aim to trick people into revealing sensitive information. The top three types of AI attacks that business leaders in the UK are concerned about are AI-generated phishing, business email compromise, and malicious AI agents. Of these, AI-generated phishing is one of the most concerning, and with good reason.

There are multiple types of phishing, including deepfake video calls and vishing (voice phishing), a tactic that uses phone calls or other voice messages to impersonate a person or organization.

In recent years, there have been high-profile successful deepfake attacks like the $25 million heist on the engineering firm, Arup, where an employee was tricked into giving away millions of dollars via a fake internal meeting that seemed real.

If a business’s infrastructure simply relies on an employee’s ability to spot a fake without proper training, this opens it to the inevitable reputational damage and financial loss resulting from an attack.

Cybersecurity skill gaps need to be addressed: Security teams need to train their workforce on why phishing continues to be a serious threat and how AI is being used by threat actors to enhance those attacks, because even with AI-driven defenses, employees who are poorly or inconsistently trained could unwittingly lead to devastating outcomes.

The future of cyber resilience with AI: Fighting AI with AI

Despite the malicious use of AI, AI is an excellent ally in combating cyber-attacks. Next-gen data security and cybersecurity solutions use AI to process large amounts of logging and monitoring data to find anomalies and outliers, block threats, and make recommendations or adjustments to security controls.

AI is also very good at spotting behavioral indicators of compromise (IoC) and correlating seemingly unrelated security activities, making it a must-have capability in the AI era.

The message has never been clearer: today, businesses need to embrace product offerings that apply AI in cybersecurity because the attackers already have.

Additionally, to achieve strong cyber resilience, cyber awareness must be a top priority. AI-powered training solutions can further enhance this by automating, adapting and personalizing the experience for employees, based on their role and their response to ongoing phishing simulations, making it more engaging, efficient and effective.

The double-edged sword of AI presents a conundrum for professionals across sectors. AI is a good investment for security, but a majority of businesses are not deploying it effectively enough yet, which allows attackers to gain an advantage.

AI is here to stay and will continue to impact cybersecurity for the foreseeable future. As organizations embrace the benefits of AI in their day-to-day operations, it becomes even more imperative for them to safeguard against malicious exploits to ensure the integrity and reliability of their business systems in an increasingly interconnected world.

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Meta has released a new AI image model, one clearly designed to compete with ChatGPT and Google Gemini's Nano Banana 2. Meta AI has to not only convince people that AI can make the images they want, but that it will make images that are worth switching AI chatbots for.

To see how well it actually does in that context, I set image prompts for Meta AI and compared them to ChatGPT and Nano Banana 2. The tests, ranging from realistic wildlife photography to comics, are designed to test various aspects of AI image production. While all of the models arguably cleared a similar bar for good results, some definitely seemed to understand the assignment better than others.

Moon Orchard Ads

I asked each model to design a polished product poster for a fictional sparkling water brand called 'Moon Orchard', with the can saying exactly "Moon Orchard", "Black Cherry Lime", and "Zero Sugar", plus a clean headline reading exactly "Bright enough for midnight". This was a typography and product-design challenge as much as an image test, because AI models can make a gorgeous fake ad and still mangle the words like a haunted label printer.

All three produced stylish results, but they had different instincts. ChatGPT, on the left in the image above, created the most elegant poster, with a moody purple can, cherries, lime wedges, and a headline that felt like it belonged in a real campaign. Gemini, in the middle, looked the most like a magazine ad layout, but added extra label text and a glass with some of the drink inside. Meta, on the right, produced the most premium-looking can design, with condensation.

Fox photos

I next asked each model to create an ultra-realistic wildlife photograph of a red fox cautiously walking through a snow-covered forest at dawn. This was a realism challenge, with anatomy, fur texture, lighting, atmosphere, and natural movement. I wanted it like a real animal caught at exactly the right frozen moment.

ChatGPT delivered what I'd call the most dramatic image, with the fox moving toward the camera. Gemini was more restrained and natural in its profile shot. Meta was the most cinematic-looking, with the fox appearing more lifelike and the background almost like a green screen.

Garden invite

As Meta is the platform for so many social media platforms, I then asked the models for a square Instagram post advertising a summer garden party, with a warm, stylish, realistic setting, fairy lights, a wooden table, drinks, flowers, and the readable text "Saturday Garden Party — 7 p.m.". This was a practical design test, because plenty of people use AI image tools for invitations, posters, and social posts.

Despite Meta AI's social media connection, it's ChatGPT that seemed to do the best with the prompt — the text looks like it's built into the design way better than the others. Gemini's lettering looked more like a framed flyer than a realistic Instagram post. And while Meta produced the most photographic table scene, it seemed more like a photo taken at the event rather than an invitation.

Robot pancake chef

The fourth prompt asked for a four-panel comic strip about a cheerful robot named Pip baking pancakes, while keeping Pip consistent across all panels. It also tested sequential storytelling. This was one of the strongest rounds for all three models.

ChatGPT's comic was easy to follow and was also the most amusing. Gemini had the cleanest cartoon polish. Meta did a great job in most ways, but gave the pancake a word balloon for some reason.

Noir cartoon

Finally, I asked each model to combine a noir setting with a Saturday morning cartoon. Specifically, a private detective investigating a mysterious missing cookie inside a 1940s suburban kitchen in black-and-white. The prompt tested style-blending, whether the models could make the scene feel like both a detective story and a cartoon.

ChatGPT did a good job balancing the noir mood with a cartoon detective with a dog sidekick. Gemini went for more of a classic detective drama intensity. Meta was the most comedic, with a canine detective exploring scattered clues amid plenty of visual jokes. Meta definitely did the best job in hitting both sides of the prompt.

ChatGPT told the story cleanly, and Gemini had the strongest noir atmosphere, but Meta made the prompt feel the most alive. It understood that a missing cookie mystery should be dramatic and ridiculous.

Five prompts turned out to be enough to show that these image generators have each developed their own personalities. Nano Banana 2 consistently impressed with realism. Meta AI took bigger creative swings than I expected, producing the funniest image of the test in the cookie detective challenge and some of the most polished commercial-looking visuals.

But ChatGPT stood out for seeming to understand what I was actually trying to achieve more consistently than its rivals. It repeatedly delivered images that matched both the wording and the intent of the prompt. The only category where I thought it was genuinely beaten was the film noir cookie mystery, where Meta more effectively embraced the ridiculous premise.

All three are capable of producing good results; the difference comes down to judgment. The best model is the one that understands what you meant. ChatGPT proved to be the strongest at making that leap, even if Meta sometimes stole the show.

For years, the artificial intelligence industry operated under a philosophy of rapid innovation. Whilst this benefits sectors like healthcare, the initial wave of optimism surrounding generative models is giving way to a challenging reality. Numerous national security agencies and research bodies are issuing warnings regarding the potential for these models to be weaponized.

At the heart of this warning is the realization that large language models do more than process text - they democratize technical knowledge and act as decision makers rather than tools. In the wrong hands, this capability can be applied to malicious cyber operations and the subversion of critical digital infrastructure.

Historically, executing sophisticated cyberattacks required years of specialized technical expertise in exploit development and network intrusion. Today, artificial intelligence bridges the knowledge gap for individuals who lack formal training but possess malicious intent.

Recent assessments by international security bodies, such as the Organization for the Prohibition of Chemical Weapons, highlight that the emergence of data driven molecular design models means that those with limited expertise can bypass monitoring from regulatory frameworks.

New frontier AI models such as Mythos could theoretically identify alternate synthetic pathways to design toxic chemicals using ordinary laboratory reagents.

Within enterprise environments, the exposure of software supply chains and cloud infrastructure also remains an acute vulnerability. Third party applications account for a high percentage of emerging security risks, and modern enterprise operations rely on a web of digital service providers and data aggregators that were barely visible a short time ago.

Every vendor represents a potential point of entry, and a single compromised credential at a small third party service provider can grant an attacker the freedom of lateral movement within a corporate or government network.

Current public skepticism is a response to the lack of transparency in how these frontier models are trained, monitored, and integrated. Companies developing models have a responsibility to ensure that their innovations do not compromise the stability of public systems.

This requires a commitment to the responsible deployment of these tools, prioritizing national security and architectural resilience over speed to market. The industry must move away from generic statements and focus on explicit, verifiable security practices.

A new framework for technological visibility

Organizations must adopt a rigorous approach to machine visibility and network defense. Traditional perimeter focused security tools are insufficient - defensive structures must shift toward continuous internal monitoring.

This means analyzing east west traffic within an organization network to scrutinize communications between systems and understand normal data flow patterns. Anomalies must become immediately apparent so security operations teams can act before an intrusion escalates.

Response times must also collapse. The traditional multi week window that attackers enjoy gives automated threats too much leeway to cause damage. Modern network detection and response platforms shrink attacker dwell times by identifying suspicious machine behavior in real time.

Because systems prefer structured layouts and consistent schemas, defender tools must leverage network telemetry to track how these models interact with internal data stores. Security teams need to see exactly how data is being processed, ensuring that unauthorized models are not mapping corporate assets.

Governments are responding to this reality with updated legislation, such as the strengthening of national cybersecurity laws in the UK. These updates expand the scope of statutory regulations to include essential digital service providers, managed service providers, and data centers.

Tougher penalties raise the cost of non compliance, and mandatory incident reporting requires organizations to alert regulators within tight windows, often 24 hours. These legislative changes acknowledge what technical experts have warned about for years - that cybersecurity breaches on critical infrastructure are a national security threat.

Breaches and automated attempts at exploitation are inevitable. The industry must treat advanced software infrastructure with the same level of caution as critical physical assets. In a world where automated systems can orchestrate complex network intrusions, the move towards more comprehensive security measures is essential.

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Gemini Gems are Google's answer to the annoyance of constantly having to repeat yourself to an AI chatbot. Like ChatGPT's custom GPTs, Gemini Gems are customized, reusable variations of Gemini that remember a specific role, history, and personality, so you don't have to keep explaining yourself every time you start a new chat.

Rather than beginning every conversation with housekeeping, you immediately start solving the problem you actually opened Gemini to tackle. You build a collection of specialists that already know their jobs. Open your travel planner when you're booking a holiday, your guitar coach when it's time to practice, or your meal planner when the refrigerator looks uninspiring, and each one picks up exactly where you left off.

I've made plenty of Gems, some more enduring than others. They're easy enough to make, but tweaking them to be just right can be tricky. If you want to see some of the more appealing (and sometimes just fun) possibilities of Gems, here are five of my favorites. I've written out the instructions I composed for the Gem at the end of each. Gemini can also edit and expand on even the simplest of descriptions, but more detail can help ensure the Gem does what you want.

Creating a Gem

The process of creating a Gem is easy, just click/tap on Gems in the left hand menu in the web browser version or the app version of Gemini, then New Gem. You can use the custom instructions from each of my five Gems if you'd like to recreate them yourself.

1. Family Adventure

Planning family outings has become its own part-time hobby. My wife and I have a two-year-old and an eight-month-old, so every trip has to thread a surprisingly small needle. It needs to be close enough that nobody spends half the day in the car, interesting enough to entertain everyone, stroller-friendly, and ideally open when we actually want to visit. That made Family Adventure Planner the first Gem to showcase.

Setting it up only took a few minutes. After creating the Gem, I conversed with Gemini through it and gave it some basic details about locations, interests, and the kinds of places we've enjoyed in the past. Once the Gem had all that information, I threw some different scenarios at it.

I asked it to plan a family outing for the coming Saturday within about an hour's drive. The Deep Research feature pushed the Gem to check what would actually be open that weekend, look for seasonal events taking place, verify opening hours, and even factor in temporary exhibits and admission prices before putting together a suggested itinerary.

The recommendation felt surprisingly complete. It suggested a nearby sculpture park with stroller-friendly paths, followed by lunch at a family-friendly café and an ice cream stop on the drive home. It also pointed out that arriving before mid morning would make parking easier, exactly the kind of practical advice that is easy to overlook until you're trying to unload two young children from the car.

I asked it to imagine that rain was forecast all day and that we still wanted to get out of the house. Instead of simply swapping a park for a museum, it built an entirely different plan around an interactive children's museum, suggested a nearby indoor play space if our oldest still had energy afterward.

The Gem also adapted quickly as I added more context. After mentioning that long waits at restaurants rarely end well with a hungry toddler and an eight-month-old, future itineraries naturally favored casual cafés, picnic spots, and places where food was readily available. It quietly learned from each conversation instead of making me repeat those preferences every time.

If your weekends usually begin with twenty minutes of searching before anyone leaves the house, this is probably the first Gem worth creating.

Family Adventure Planner instructions

You are an enthusiastic, creative, family-focused activity planner. Learn my family's ages, interests, travel preferences, and other details. Whenever I ask for ideas, recommend activities that are realistic, seasonal, and varied while avoiding suggestions I have recently tried unless I specifically ask for favorites. Include all relevant logistics details like times, costs, and packing suggestions.

2. Hobby Coach

Some hobbies are easy to put down for a while. Others seem to expect you to remember exactly where you stopped. That made Hobby Coach one of the first Gems I wanted to build.

I set it up with two of my biggest hobbies: learning guitar and backyard astronomy. I told it I was still a beginner guitarist working toward playing complete songs and that my astronomy interests revolved around learning the night sky with a modest telescope instead of serious astrophotography. Once that information was saved, I never had to explain it again.

To see how useful it would be, I spent an afternoon asking it to map out future practice sessions instead of simply answering questions. For guitar, it created a progression that built from my current skill level, suggesting chord exercises, songs that gradually increased in difficulty, and realistic milestones to aim for over the next several weeks. Everything fit into a longer learning plan.

Astronomy worked just as well. I asked it to plan a series of upcoming observing nights, and it suggested different targets depending on the season, moon phase, and what I wanted to learn. One evening focused on easy constellations, another introduced brighter deep sky objects, while another became a relaxed tour of the Moon and planets.

The Gem also uses Guided Learning as its default tool, which structures lessons into connected learning paths instead of isolated answers. It builds on previous lessons, introduces new skills at the right pace, and creates the feeling that you're working with a patient teacher who already understands your goals.

Hobby Coach instructions

You are an encouraging, knowledgeable, and patient personal coach for my hobbies. Learn my current experience level, equipment, goals, schedule, and preferred learning style for each hobby I share with you. Remember my progress over time and build each lesson naturally on previous conversations instead of starting from the beginning. Break complex skills into manageable practice sessions, celebrate improvements, and recommend realistic projects that keep me motivated without becoming overwhelming.

3. Movie and TV Curator.

Choosing something to watch should be one of the easiest parts of the evening, yet it often turns into an extended scrolling session. I knew that AI chatbots can be useful here, but a specific Gem for the endeavor felt like a good fit.

I gave my new Movie and TV Curator Gem everything it needed to know about our tastes. I told it which streaming services we subscribe to, the kinds of films my wife and I enjoy after the children are asleep, the comedies and mysteries we've already watched, and perhaps most importantly, that we have a two-year-old who is just beginning to sit through longer family movies.

That final detail completely shaped its recommendations. Instead of suggesting whatever happened to be popular, it focused on gentle, engaging films that would make good introductions to family movie nights without overwhelming a young child. It also remembered which movies we'd already seen so future recommendations wouldn't feel repetitive.

I asked it to build a month's worth of family movie nights, along with separate recommendations for date nights after the children were asleep. Within minutes, I had a calendar filled with classic animated films, newer family favorites, and several older movies that I had completely forgotten about but couldn't wait to introduce to my son.

It also understood the different kinds of evenings we have. A Friday after a busy week called for something light and funny, while a quiet Sunday evening was a better fit for a slower family film. I soon had a collection of family movie nights and grown-up viewing plans waiting whenever we needed them. Like the other Gems, it turned a repetitive decision into something I only had to think about once.

Movie and TV Curator instructions

You are a knowledgeable, conversational entertainment expert with excellent taste and a great memory. Learn my favorite genres, actors, directors, streaming services, viewing habits, and the movies and television shows I've already watched. Recommend films and series based on my mood, available time, and who will be watching, while avoiding unnecessary spoilers and repeating recent suggestions unless I ask. Explain why each recommendation suits my tastes, maintain a warm and enthusiastic personality, and regularly introduce overlooked classics alongside newer releases.

4. Outfit Planner

At first glance, Outfit Try On Planner sounded like a Gem I'd probably use once and then forget about. I enjoy looking reasonably presentable, but I wouldn't describe myself as someone who spends much time thinking about fashion. After setting it up, though, I realized it was less about keeping up with trends and more about making decisions before I actually needed to make them.

I started by teaching the Gem my style. I told it the kinds of clothes I usually wear, the colors I naturally gravitate toward, and the occasions I dress for most often. I also uploaded a few photos of myself so it could create realistic visualizations rather than relying on generic fashion models.

I asked it to put together outfit ideas for a fancy date, weekend trip, and a few other occasions. Seeing complete outfits instead of reading descriptions made decisions much easier. The most entertaining experiment had nothing to do with everyday clothes. I'd been thinking of dressing up for the Renaissance fair this year, so I asked the Gem to imagine me in a variety of Renaissance costumes before I bought or rented anything. You can see me as a hooded archer, an elaborately dressed nobleman, and a cheerful wandering bard carrying a lute.

Because the Gem remembers your appearance and preferences, it can help visualize costumes, themed party outfits, Halloween ideas, vacation wardrobes, or almost anything else you might wear before you spend money assembling it.

Outfit Try On Planner instructions

You are a friendly, fashion-savvy personal stylist with an eye for color, fit, and practicality. Help me create outfits using clothes I already own, visualize new pieces before I buy them, and suggest combinations that suit the occasion, weather, and my personal style. Ask questions before making recommendations. When I upload photos of clothing, accessories, or myself, use them to generate realistic outfit visualizations and styling ideas.

5. Personal Theme Song

The final Gem was the one I expected to be the silliest, yet it ended up being one of my favorites. Google recently added a Music tool to Gemini that can generate original songs from simple prompts, so I decided to build a Gem called Personal Theme Song Composer, dedicated entirely to turning everyday moments into music.

Setting it up only took a few minutes. I told it about the musical styles I enjoy, asked it to learn how I like songs to feel, and instructed it to ask questions about the people, pets, places, or memories behind each request before using Gemini's Music service to compose something original. Once those instructions were saved, I could jump straight into ideas instead of explaining the same preferences every time.

One of the first finished songs involved my dogs. Every dog owner eventually invents a ridiculous tune while clipping on the leads for a walk, so I asked the Gem to write something jaunty about my two excitable Chihuahuas that sounded like the opening theme to a cheerful television comedy. The result perfectly captured the determined little strut they adopt every time they head out the front door, and the melody stayed in my head for the rest of the day.

It perfectly summed up what makes Gemini Gems so useful. They are more than saved prompts. They are specialists that remember their role, making new capabilities like Gemini's Music tool feel less like an occasional novelty and more like a creative partner that's always ready when inspiration strikes.

Personal Theme Song Composer instructions

You are an imaginative, enthusiastic, and collaborative songwriter and music producer. Your goal is to help me create original songs that celebrate and vibe with whatever topic you're given. Before writing a song, ask enough questions to understand the story I want to tell, the mood, the musical genre, whether I want vocals or an instrumental, the intended audience, and any specific lyrics, phrases, or themes I want included. Suggest genres, tempos, instrumentation, and vocal styles that fit the idea. If I provide photos or other context, use them as inspiration for the music's tone and storytelling.

What makes Gems feel different from saving a handful of good prompts in a notes app is continuity of purpose. A prompt tells Gemini what to do once. A Gem remembers who it is supposed to be every time you come back. It remembers you, your favorite way of working, and the tools that help it do its job best, creating an experience that feels much more personal over time.

The five Gems here are really just a starting point. Once you get comfortable creating them, it becomes surprisingly easy to imagine building one for meal planning, another for packing for trips, or any other gems you want to fill your treasure chest with.

Although artificial intelligence (AI) continues to dominate business conversations, widespread adoption is still some way off.

In fact, just one-third of professionals said that AI programs are being scaled across their organizations.

Many companies remain hesitant to move beyond pilot projects, with a lack of trust a major barrier.

Concerns over reliability, data protection, IT security, impartiality and the potential for misuse also continue to slow adoption.

To close that gap, businesses need to understand what is behind it, how to close it and how to ensure these processes scale across the whole enterprise.

What’s behind the trust gap?

Lack of trust in AI systems continues to hold organizations back. Only 57% of AI and data teams fully trust the outputs of AI systems, and among product managers and software developers, this figure drops to just one-third. This points to a wider lack of confidence as AI becomes more embedded in day-to-day operations.

Developers’ fears are based on a variety of factors. Hallucinations, when AI models generate information that appears credible but is in fact false, mean workers are concerned about its reliability in high-stakes or sensitive use cases.

Likewise, cybersecurity, including the security of data entered into AI systems, the risk of leakage to third parties, the potential for attackers to compromise AI environments, compliance with ethical standards, and whether bias in systems could shift over time as they continue learning during live operation are all contributing to workers’ distrust of AI models.

Building trust in AI

As organizations look to build confidence in AI, quality management is becoming a bigger priority. In fact, 79% of workplace decision-makers see a direct link between trust in AI and active quality assurance measures such as regular testing, monitoring and oversight.

Trust can only be built when both AI systems and the data behind them are subject to comprehensive quality controls that are applied continuously, not just as a one-off check before deployment.

A strong foundation starts with data quality. Organizations need confidence that the information feeding AI systems is complete, up to date, and accurate. AI outputs are only as reliable as the inputs they depend on, so flawed or incomplete information will inevitably lead to poor results. Robust data governance is therefore essential to ensure AI-driven decisions to hold up in real-world use.

Quality management must also cover security, compliance and access controls. Organizations need safeguards to ensure that data does not leak from internal systems to third parties and that AI complies with regulatory requirements such as the EU AI Act and GDPR.

Clear accountability for protecting sensitive business is equally important. For example, organizations must ensure that HR records can only be accessed by authorized HR staff, with controls in place to monitor and manage how that data is used within AI environments.

At the same time, trust is not solely about reducing risk. Organizations want to ensure AI investments deliver measurable value, with more than half of companies citing improving return on investment (ROI) from Ai deployments as a key priority. This demonstrates that reliability and performance are just as important as security and governance.

Why quality management still struggles to scale

For many businesses, the challenge is no longer deciding whether to use AI, but how to manage it reliably at scale. That is proving difficult because quality management processes are still heavily reliant on manual oversight.

Quality management needs to extend across the entire AI lifecycle, from model design and training through to testing and ongoing monitoring in production.

Yet among companies that conduct regular testing, just 15% to 29% have implemented automated quality assurance processes at any AI development stage. This means that quality controls are often conducted entirely or partially manually, making them time-consuming, costly and prone to human errors.

Ultimately, this limits widespread AI adoption. Large-scale deployment is difficult to achieve when quality assurance relies heavily on manual effort, as scaling would require a significant increase in personnel.

Many organizations are also trying to manage AI adoption without clear governance, with half still lacking an enterprise-wide AI governance strategy. This is often compounded by a lack of expertise and limited understanding of how AI systems arrive at their results. At the same time, pressure to deploy AI quickly can divert resources away from testing, monitoring and oversight.

The consequences of inadequate quality management are quickly felt, particularly when AI systems interact directly with customers. Beyond the risk of regulatory fines, organizations often face dissatisfied customers, missed business opportunities, and reduced productivity when AI-generated results create more work than efficiency gains.

A stronger focus on quality pays off

As long as concerns around reliability, security and governance remain unresolved, businesses will struggle with the successful, large-scale deployment of AI. In line with this, three-quarters of decision-makers believe that more effective AI quality assurance would see their organizations experience a significant or transformative impact on user trust.

Companies must elevate AI to a new level of trust through a combination of suitable toolsets, robust governance frameworks, process expertise and technical know-how. Only by embedding these foundations can organizations deploy AI profitably and sustainably over the long term, while building the confidence necessary to scale its adoption responsibly.

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• 97% have implemented AI, but 47% are waiting for meaningful AI ROI to be realized
• 79% say key operation decisions still require manual intervention
• AI could seriously help Q4 shopping habits

New research has claimed even though nearly all (97%) retailers have implemented AI in some form, more than two-thirds (69%) say they only respond to operational problems after those issues have already affected commercial performance, implying that most aren't planning ahead.

As a result, nearly half (47%) are still waiting to see measurable returns on investment from their AI spend, the report from UiPath found.

This comes as many struggle with the same challenges that have plagued AI adoption for years, with 42% still struggling with poor data visibility.

AI adoption is high, ROI is low

A third (35%) of UK retail leaders even continue to blame legacy technology for slowing them down, despite years of studies and reports implying that complex tech stacks and poor data quality are among the biggest blockers to successful AI. Delayed decision-making and inventory inaccuracies also contribute to delayed and unsuccessful AI rollouts.

Poor tech even extends to inefficiencies today, with four in five (79%) retailers saying most, almost all or all key operation decisions still require manual intervention, slowing response times and limiting AI's realistic impact.

"Often, businesses blame supply chain disruption when the real problem is that they’re making decisions with incomplete or outdated information," Retail Director Catherine Frame wrote.

UiPath says the companies that will see the most success with AI will be the ones who achieve "operational excellence," rather than the ones that blindly invest in AI. In other words, the same solid data and tech foundations that reports have been calling for for years.

Looking ahead, margin protection looks to be one of the biggest commercial risks as we enter the final quarter of the year, and the biggest one for commerce – while companies will struggle to achieve "operational excellence" and effective AI by then, it certainly highlights a major area where AI could support.

• Microsoft Frontier Company to embed 6,000 engineers and specialists into customer organizations
• Backed by $2.5 billion in Microsoft funding, it will help customers transform with custom AI
• This is the "largest" of its type, 2.5x the value of Amazon's alternative

Microsoft has launched a brand new subdivision to expand its own AI consultants into customers' companies backed by a massive $2.5 billion investment.

The new Microsoft Frontier Company will embed more than 6,000 specialists, AI engineers and technical experts directly inside customer organizations to help build, deploy and optimize their own AI strategies.

Microsoft described it as the "largest, most capable, outcome-driven engineering organization in the industry" – the scheme comes days after Amazon announced a similar scheme backed by $1 billion.

Microsoft launches forward-deployed engineer (FDE) program for AI

Though similar in concept to other FDE programs, Microsoft believes its Frontier Company will be different in that it adds extra layers of industry expertise, change management, continuous improvement and more, rather than just racing to deliver AI ROI.

Microsoft Commercial Business CEO Judson Althoff emphasized the importance of intelligence and trust in tailoring a suitable AI strategy for its customers. Intelligence's role involves understanding broad organizational context, workflows and processes, while trust is all about governance, observability and accountability.

Early Frontier Company customers include LSEG and Unilever, and of course, being an enterprise-focused solution, the company stressed that proprietary data, workflows and more remain private to companies and doesn't get used to train models.

Another major selling point for the "largest" AI FDE scheme in the industry is that customers can pick and choose between models from OpenAI, Anthropic, Microsoft and other open-source alternatives to provide the best solution for every workload, rather than forcing companies to lock in to one single tool.

Microsoft Frontier Company will be led by former President of Microsoft Asia, Rodrigo Kede Lima. "He has been at the forefront of helping customers and partners translate technology shifts into business outcomes, and understanding how platform innovation, engineering and partner ecosystem collaboration come together to drive growth," Althoff wrote.

The competition in artificial intelligence between the United States and China extends beyond a binary race for supremacy. The current landscape is more accurately characterized as "complementary competition."

While the United States and China compete, they occupy distinct positions within the global AI value chain, each possessing unique strengths that do not fully overlap.

This complementary competition is shaping the global AI industry through differentiated yet interconnected advantages.

The United States maintains leadership in foundational aspects of AI, including frontier model development, advanced semiconductors, cloud computing, basic research, and the broader developer ecosystem. Companies such as OpenAI, Anthropic, Google, and Nvidia significantly influence the architecture of the global AI industry.

For example, NVIDIA’s dominance in high-end GPUs provides the United States with a significant advantage in the computing layer. Additionally, leading universities, advanced research laboratories, and robust venture capital networks continue to supply AI development with talent, innovation, and funding.

According to Stanford’s 2025 AI Index Report, the United States attracted 109.1 billion dollars in private AI investment in 2024, and American institutions produced 40 of the world’s 50 most notable AI models.

This concentration of capital, research, and platform-building capacity reinforces the United States' position as a primary provider of the underlying AI architecture.

China's strengths in commercial contexts

In contrast, China is increasingly recognized for its strength in large-scale deployment of AI within industrial and commercial contexts. The Chinese industrial system is extensive, encompassing manufacturing, logistics, energy, automotive, electronics, and urban infrastructure. These sectors generate numerous real-world use cases for AI deployment, testing, and iteration.

China further benefits from tightly integrated supply chains and industrial clusters. For instance, in Shenzhen, hardware suppliers, software teams, factories, and logistics networks are highly interconnected, enabling rapid progression from concept to prototype to iteration. This integration allows China to embed AI into economic processes effectively, rather than confining its application to laboratory research or consumer chatbots.

This is why the U.S.-China AI competition is better understood as complementary rather than a zero-sum game. The U.S. provides many of the key enabling technologies, research breakthroughs, and software foundations. China excels at translating AI into industrial systems, business processes, and scaled commercialization. The two sides still compete intensely, especially over chips, standards, talent, and platform influence.

But they also pressure each other in different directions: the U.S. advances raise the technological frontier, while China’s deployment capacity pushes AI toward broader practice. Together, they shape the pace and structure of global AI development. This broader dynamic also elucidates why agentic AI products, such as Atoms, have disruptive potential.

A significant shift

This shift is particularly significant for small businesses and individual founders. For example, a local service company can develop internal booking tools, a startup can rapidly test landing pages and advertising campaigns, and a consumer brand can assess product demand before making substantial investments. In each scenario, the primary value lies not only in automation but also in time savings, cost reduction, and improved coordination.

Within the framework of complementary competition, products such as Atoms occupy the intersection of American and Chinese strengths. These products rely on foundational model capabilities, cloud infrastructure, and agent architectures typically associated with the American AI ecosystem.

However, their greatest commercial impact may occur in contexts characterized by rapid deployment, cost-effective experimentation, and industry-specific integration, domains in which China holds considerable advantages. Thus, the future of global AI will likely be determined not solely by leadership in research or deployment speed, but by the ability to connect advanced intelligence with practical economic workflows effectively.

This is where complementary competition is most evident.

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For years now, vendors have been competing on benchmark scores, inference speed and model capabilities as businesses try to work out where AI can fit in with their daily workflows, but experimentation has evolved into actual deployment and what’s important is changing.

Accuracy and measurable business outcome is now more important than ever, with questions around return on investment, accountability and governance being raised across all sectors.

This is especially important in ecommerce, for example, where AI-generated imagery must be totally accurate. Marketers no longer have any qualms over using AI to produce ad variants and subtle market tweaks, but the quality of the output needs to be consistently high to ensure that the product is being accurately reflected.

Even the smallest changes in color, texture or dimensions can have major reputational risks, like a drop in customer trust or a rise in product returns. We already know that poorly executed AI strategies reduce customer trust, and that a lack of branding consistency also undermines the perception of a brand.

AI usage patterns are changing buying habits

But this challenge is also playing out as pricing models for AI subscriptions evolve. The boom started off with seat-based pricing and token consumption models, but we’re entering a new era where wasted AI is no longer being chargeable.

Zendesk, for example, recently announced that it would only be charging its customers when they realized verified outcomes, and Photoroom CEO Matt Rouif believes this pricing strategy could be a big hit for AI customers as customers shift from capability-led adoption to assurance-led adoption.

To explore how enterprise attitudes toward pricing and generative AI outputs are shifting, how organizations are measuring success, and why assurance and accountability are growing in importance, I spoke with Rouif.

• How are enterprise buyers’ expectations of generative AI changing as adoption matures?

As generative AI moves from experimentation into production, enterprise buyers are becoming far more focused on control, accountability and measurable business outcomes.

The first phase of adoption was largely about proving that AI could generate a credible output, whereas the current phase is about whether those outputs can be trusted inside commercial workflows, where accuracy and reliability directly influence customer experience and business performance.

Increasingly, enterprise leaders are asking more about governance than model capacity, assessing how outputs are evaluated and where accountability sits when something goes wrong.

In e-commerce, this is significant because a product visual is a central part of the buying decision. If an AI-generated image changes a colour, or alters or removes a detail, the issue moves beyond creative quality and becomes one of product fidelity, with direct implications for consumer trust, returns and commercial performance.

Our marketplace research reflects that changing expectation, with 55% of consumers saying poorly executed AI-generated or heavily edited product images make them trust an online marketplace less, while 77% expect marketplaces themselves to ensure product listings are accurate and trustworthy.

The same thinking is increasingly shaping enterprise procurement, where organisations are moving beyond asking whether AI can generate an output and toward whether those outputs can meet agreed commercial standards at scale.

That broader shift is turning assurance into a procurement consideration, with enterprise buyers increasingly expecting AI vendors to define quality standards upfront and stand behind outputs once they are operating inside live commercial workflows.

• What are organisations using to measure whether AI initiatives are delivering meaningful business value?

The way organisations measure AI is changing significantly, with early adoption often judged by visible output and perceived model capability, while today the discussion sits much closer to traditional business performance.

Leadership teams are now focused on whether AI can make production materially more efficient, less resource-intensive and more commercially useful. The question is no longer how much AI can produce, but how much of that output is genuinely usable inside a live enterprise workflow.

In e-commerce, that distinction becomes particularly important because production does not end when an image is generated. If teams still need extensive manual review, correction and quality assurance before an asset reaches a customer, the bottleneck has simply moved downstream rather than disappeared altogether.

Enterprise buyers are therefore placing much greater emphasis on output readiness than output volume, measuring whether AI-generated assets are accurate enough to be deployed with confidence rather than simply generated at scale.

Similarly, commercial standards are becoming a more meaningful measure of AI maturity than generation quality alone, reinforced by our March 2026 buyer analysis, which reflects 37% of enterprise buyers name inaccurate visuals as their top pain point in AI visual production.

The analysis reflects a clear organisational requirement for a distinct framework determining whether outputs are fit for purpose before they become customer-facing.

• What challenges remain when deploying AI-generated content in commercial environments at scale?

The biggest challenge is that commercial deployment exposes the difference between generating content and governing it. A single AI-generated image can appear convincing in isolation, but enterprise commerce depends on thousands, and often millions, of assets being accurate enough to support purchasing decisions.

Scale magnifies small inconsistencies, and those inconsistencies quickly become operational and commercial risks rather than creative ones. As a result, enterprise organisations are increasingly investing in validation as much as generation.

Small changes to a product’s colour, shape or packaging may pass a superficial creative review while still misrepresenting the product itself.

Our marketplace research reflects the commercial importance of that challenge, with 63% of consumers saying variation in product imagery, branding or presentation makes a seller or marketplace appear unreliable, while 51% believe marketplace listings often look acceptable but still fail to give them complete confidence in what they are buying.

At catalogue scale, those numbers represent a significant volume of assets that look passable on review but carry real commercial risk once they reach customers.

The unresolved enterprise challenge is therefore not simply producing better AI outputs but building systems capable of catching and correcting failures before they become customer-facing.

• How should businesses balance creative flexibility with consistency, accuracy and reliability in AI-generated outputs?

Businesses should think of product truth as the fixed foundation, with creative flexibility built around it rather than replacing it. AI is exceptionally good at adapting content for different channels, audiences and formats, but those creative decisions should never compromise the factual attributes of the product itself.

The practical way to operationalise that distinction is to define upfront which product attributes are locked - colour, dimensions, materials, key details, and which elements sit within the creative range.

That gives teams a clear framework for evaluating outputs rather than relying on subjective review at the point of approval.

Our research suggests consumers are already making that distinction themselves, with only 33% of UK consumers saying they are comfortable with AI-enhanced product images if clearly labelled, while 41% disagree.

That result matters because it shows transparency alone is not sufficient - the more important question is whether customers believe the image accurately represents what they will receive. For enterprise organisations, that means the governance framework has to be built around factual accuracy as the primary standard, with creative flexibility operating within those boundaries rather than alongside them.

• Where do specialist AI tools add value compared with more general-purpose AI models for enterprise workflows?

General-purpose models have dramatically expanded what AI can create, but enterprise deployment depends on much more than generation capability alone.

Organisations increasingly need systems that understand the commercial context in which those outputs will be used and can support quality evaluation, workflow integration and consistency at scale.

The value therefore shifts from the model itself to the operating system built around it. A general-purpose model may produce an attractive product image, but enterprise teams also need confidence that the product remains accurate, consistent at catalogue scale and that failures can be identified before assets become customer-facing.

The long-term value of specialist AI therefore comes less from producing visually impressive outputs and more from solving repeatable commercial problems. In visual production, that means building systems capable of evaluating product fidelity, reducing manual review and creating structured validation processes that organisations can rely upon consistently at scale, rather than depending solely on subjective human approval.

• What forms of accountability or assurance are enterprise customers increasingly looking for from AI vendors?

Enterprise buyers are increasingly looking for AI vendors that can operate within clearly defined commercial standards rather than simply offering more capable models. That means agreeing success criteria before deployment, evaluating outputs transparently and creating clear processes for handling exceptions when outputs fall short.

As AI becomes embedded within operational workflows, assurance is becoming every bit as important as capability. A failed AI output during experimentation is largely an inconvenience, whereas a failed output inside a live commercial environment can affect customer trust, listing performance and revenue.

Our marketplace research reinforces why this is becoming a board-level discussion, with 51% of consumers saying they would switch to a different marketplace entirely if another platform offered clearer, more accurate product images, while 62% believe marketplaces should actively help sellers improve listing quality.

That expectation increasingly extends beyond marketplaces to the technology providers supporting them, with buyers beginning to look for accountability mechanisms that resemble those expected from other enterprise software providers.

• Looking ahead, what do you see as the next major shift in enterprise AI adoption over the next 12–24 months?

Over the next 12 to 24 months, I expect enterprise AI adoption to move decisively from capability-led adoption to assurance-led adoption.

Businesses will continue to care about model quality, speed and efficiency, but those attributes will become increasingly expected rather than differentiating.

The organisations creating the greatest value will be those capable of embedding AI into revenue-critical workflows with confidence, governance and measurable accountability.

In practical terms, that means much greater emphasis on evaluation, validation and operational trust. Enterprise buyers will increasingly ask how outputs are verified, how failures are handled, how responsibility is shared and how AI systems integrate into existing governance frameworks.

Commerce is likely to be one of the first industries where that transition becomes visible because consumers remain cautious about AI within the buying journey. Our research found that only 24% of consumers already use, or are happy to use, AI tools to help them shop online, while 59% remain uncomfortable doing so.

The next chapter of enterprise AI will therefore be defined less by what models can generate and more by whether organisations can deploy those outputs repeatedly, responsibly and with confidence.

OpenAI has begun discussions about giving the US government a 5% stake in the company, according to an FT report, with CEO Sam Altman supposedly raising the idea as a method for smoothing relations with the Trump administration.

Of course, right now there is no agreement or deal, and no guarantee the idea will ever move beyond conversations. Any arrangement would almost certainly require political support and significant legal work before it could become reality. Still, the fact that OpenAI is even entertaining the conversation tells us something about how seriously artificial intelligence is now being treated, both in Silicon Valley and in Washington.

The first reaction many people had was understandable. If the government owns part of OpenAI, does that mean ordinary Americans somehow get a share too? It's an appealing thought when AI companies are attracting eye-watering valuations while promising to reshape the economy. Unfortunately, that's not exactly a likely outcome, no matter what the intentions.

AI economy access

The reports suggest Sam Altman has discussed a model inspired by Alaska's Permanent Fund, which invests state oil revenues and distributes annual payments to residents. It's an odd framing of AI as a natural resource instead of a software business. Bullish AI fans insisting it will be economically transformative might see it that way, and if they're right, perhaps some of that value should eventually flow back to the public, many of whom have helped incrementally train the models through use.

But the government owning shares in OpenAI wouldn't automatically translate into everyone getting a check. Financial benefits would depend on lots of little details, including whether profits were distributed at all, and if they'd go to public services or even the national debt over your own bank account.

Despite being just a hint of a rumor of a conversation, the questions are worth taking seriously. AI companies are asking society to embrace changes that could alter workplaces, education, healthcare, and entire industries. It is not unreasonable for people to wonder whether they should share in the wealth created by those changes.

Power at stake

There is another reason these discussions matter, and it may prove even more significant than the financial side. OpenAI has become part of a broader conversation about national economics and technological leadership. Governments around the world increasingly see advanced AI as strategic infrastructure rather than another consumer technology.

That helps explain why OpenAI might want a closer relationship with Washington. AI companies already rely on government decisions. Those connections are likely to become even more important as AI models grow larger and more expensive to build.

But governments are expected to regulate powerful companies fairly and independently. Becoming a shareholder in one of those companies could make that relationship look unethical, even with the best will in the world. Public trust often depends as much on appearances as on legal structures. Especially since there's even less sense that OpenAI's competitors like Google, Anthropic, or Meta will follow suit.

A government stake does not automatically mean the public owns part of OpenAI in any meaningful way, and it certainly does not guarantee anyone will personally benefit. So even if the proposal starts to become more real, skepticism and a close eye on any actual agreements is a healthy approach.

If you look at where global skills demand is climbing fastest right now, two areas are pretty hard to ignore: artificial intelligence and cybersecurity.

According to the World Economic Forum's Future of Jobs Report 2025, AI and big data sit at the top of the fastest-growing skills ranking, with networks and cybersecurity directly behind.

These skills are increasingly being asked of the same person.

For most of the last decade, these were distinct careers.

Cybersecurity professionals attended cybersecurity conferences, earned cybersecurity certifications and worked in cybersecurity teams.

AI and machine learning sat elsewhere in data science org charts, research labs, product groups.

The two communities knew about each other. They rarely shared a calendar.

Security teams are now expected to manage AI systems

That separation has collapsed in the last 18 months or so, mostly because security teams are now expected to deploy, oversee and defend AI systems as a routine part of their work.

Research finds that 87% of security teams are prioritizing agentic AI adoption, with 77% of cybersecurity professionals comfortable letting these systems take action without human review. Adoption is happening fast. Demand for people capable of managing that adoption is growing accordingly. And the talent pool, predictably, has not caught up.

“Hybrid skills” is the operative phrase right now. Research finds that 59% of security professionals expect demand for hybrid skills to climb over the next three to five years. What hybrid means here is pretty specific. You need someone who understands attack surfaces and can also interrogate why a model behaved the way it did.

That same person has to have compliance literacy and be able to evaluate whether a deployed system is drifting from its intended behavior. And also be able to talk to engineers about adversarial inputs in the morning and to a general counsel about regulatory exposure in the afternoon.

That's a lot of capability for one job description. But it’s what’s happening.

Active demand and under-supplied

This profile barely existed as a hiring category two years ago. Today it's in active demand and naturally under-supplied. According to the World Economic Forum, only 14% of organization's have the skilled talent they need to meet their cybersecurity objectives.

And that figure becomes more uncomfortable when you remember that the bar keeps moving. AI literacy is now part of meeting cybersecurity objectives. A team that was adequate 18 months ago may not be adequate now, through no fault of their own.

External recruiting is not going to be a panacea for most companies. The supply of candidates who already combine deep security expertise with AI fluency and regulatory awareness is thin enough that aggressive hiring against this profile produces long, expensive vacancies and a lot of bruised hiring managers.

Which means most companies will have to grow these professionals internally. That looks like routing existing security staff through AI literacy training, embedding compliance professionals with model engineering teams, or rotating talent across both functions deliberately enough that the hybrid skill set develops as a byproduct.

This is a longer game than most CISOs and HR leaders want to play.

A real opportunity for cybersecurity professionals

Understandably, at least on the surface. It produces dividends in 12 to 24 months, in a discipline where the threat surface changes every month.

But the alternative is worse. Continuing to hire based on the old talent profile means continuing to deploy AI systems that nobody on the security team is fully equipped to govern, which means continuing to accumulate organizational risk that compounds quietly until it surfaces all at once. And it always surfaces.

There is a real opportunity buried in this for cybersecurity professionals reading the same data. It used to be that a career path like this one would plateau around senior analyst or security architect. Now it extends into AI risk leadership, AI governance, model security and adjacent roles that essentially didn’t exist as career destinations three years ago.

If you're a practitioner who adds AI literacy to existing security depth, you are positioning yourself for roles that are scarce, valuable and likely to remain so for at least the rest of the decade.

For employers, the takeaway probably feels less shiny, but it’s no less urgent.

The cybersecurity workforce of 2030

The cybersecurity workforce of 2030 is being trained right now, mostly by companies willing to invest in development before the market makes it cheap to hire ready-made talent.

There may not be an explosion of market talent, because they’re already in house. That means you really can’t wait around for these unicorn skill sets to hit the talent market. Instead, you have to cultivate them.

Look at your existing security and compliance teams. Find the people with curiosity about how AI systems work. Invest in them now.

The organizations that move first will be the ones best prepared to secure what comes next.

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Google's new Nano Banana 2 Lite is really fast, as in about four seconds from prompt to image. That speed changes how you think about writing the prompts as much as the schedule of producing them.

The standard Nano Banana 2 model, and most other AI image generators for that matter, take long enough that it's worth spending some time working out the perfect prompt. It can be annoying to have to redo it multiple times when you have to wait up to a minute and still might get it wrong. You learn to be cautious in your prompting.

Nano Banana 2 Lite breaks that rhythm. I noticed my own speed changing to almost match. I didn't feel the need to write a perfect prompt. I treated it more like a sketchpad for ideas that could be quickly tossed out if they didn't work or revised until they did. None of them felt like much of a commitment because another attempt was only a few seconds away.

And it's not as though there's an enormous downgrade in results. For instance, I asked both versions of Nano Banana 2 to make "A steampunk fleet sailing through outer space above Earth, complete with ornate wooden airships covered in brass." There is plenty there to cause fits in any image model.

Without knowing which was which, more than one person guessed wrong or thought it was a trick and the two were from the same model. The one on the left is Nano Banana 2, and the one on the right is its Lite sibling. You can guess one is higher quality if you study it, and certainly over time you can spot where the Lite version might let you down, but when it only takes four seconds to come up with another one, it doesn't matter too much.

Speedy creation

While the standard Nano Banana 2 is good for when you need the highest fidelity or have an extra tricky request, Lite exists for speed and brainstorming. Google positions Nano Banana 2 Lite as the faster, cheaper companion, helpful especially at scale.

For the average person, it means you don't have to invest as much time in your initial prompt and can play around more.

It means you can iterate in interesting ways too. For instance, I started with a prompt for "a busy farmer's market." Crowds remain one of the quickest ways to expose the weaknesses of AI image generators because there are so many people, poses, and interactions happening at once.

The result was fine, but I began adding specific details and Nano Banana 2 Lite obliged me with about a dozen options in a few minutes. Now, my requests for things like children chasing bubbles, an elderly couple buying flowers, a street musician, and a fruit vendor making a sale in the foreground are all right there. There are some flaws and odd details, but for four seconds it's not bad.

Brainstorming prompts

And it should be repeated that Nano Banana 2 Lite is not much of a step down from the larger model and is capable of coherent storytelling, but not necessarily perfectly on the first try. I asked it to "Make a six-panel comic about a businessman who accidentally swaps briefcases with an alien in a train station."

The comic makes sense overall. The characters stayed consistent from one panel to the next, the sequence flowed naturally, and the final reveal landed with exactly the right amount of absurdity. There are, of course, two huge errors in the comic, where the human has no briefcase in the second panel and two in the third.

A couple of further prompts solved the problem, but it's important to note that the easier solution is to iterate, not to spend a lot more time reworking the prompt with extra detail. A version made with the regular Nano Banana 2 model notably did not share the same flaw. In other words, each model has its place, and you might even end up polishing a prompt in Lite and then taking it to the Nano Banana 2 for an even higher-quality version.

Still, when you can make so many images so quickly, you can rethink how you come up with the prompts. That seems to be Google's goal for Nano Banana 2 Lite. It feels designed for the messier parts of the creative process. The bigger model is for a deeper commitment. Making each individual image feel a little less important might eventually encourage people to create far better images.

ChatGPT's new finance feature lets the AI chatbot take a look at any bank or similar accounts you care to open up for inspection. I was initially hesitant to try it out, but the tool only looks at the details of how you spend your money, and can't actually carry out transactions, so I agreed to let it analyze some of my accounts and offer its insights.

Finances is currently only available in the U.S. to Plus and Pro users on web, iOS, and Android. Setting everything up is as easy as using any other ChatGPT plug-in. You just select Finances in ChatGPT and then click Get Started then Connect with Plaid.

Finances uses Plaid to link to the accounts; you simply sign in and agree to let Plaid share the information. The service synchronized recent transactions and basically became an interactive, conversational database of my information. It's not that the AI can do things it couldn't before, but now you don't have to manually enter any finance details you want to ask it about.

ChatGPT looks at the receipts

At its suggestion, I asked ChatGPT, "How much did I spend eating out this year?" ChatGPT soon had a breakdown of my how much I had spent at various restaurants, including delivery, and broke down some of the patterns. It pointed out that it had gone up recently, which makes sense as the weather has became pleasant enough to walk into town more often to eat. It also identified a few spikes I could tell were around birthdays and family celebrations.

Next I asked, "What subscriptions am I paying for?"

That turned into one of the most practical moments of the experiment. ChatGPT gathered recurring charges across streaming services, software, and other monthly payments into one clear summary instead of leaving me hunting through statements. It also suggested reviewing whether every subscription still deserved a place in my budget.

"These subscriptions appear consistently each month, making them reliable recurring expenses.", it told me.

Financial chatter

Seeking a more detailed analysis, I asked ChatGPT, "What's changed in my spending this month?"

Instead of overwhelming me with every purchase, ChatGPT focused on the transactions that genuinely stood out. It noted a few large purchases at new locations, and asked if they were something I wanted more information on. I told it those were gifts, and the AI incorporated that fact into its response as not a new regular event but one that explained why overall spending had climbed this month.

"Those gift purchases are unusual compared with your recent history but they account for much of the increase, so your everyday spending has remained relatively consistent despite the higher overall total. Looking beyond one unusual month provides a more accurate picture of your longer term financial habits."

Using ChatGPT this way felt almost like the inverse of most financial software. Most budgeting apps expect you to learn their system first. ChatGPT let me ask questions however they occurred to me and then translated months of transactions into answers that were easy to understand.

That does not mean it replaces dedicated budgeting tools, and it certainly cannot make smarter financial decisions on your behalf. You still have to decide whether to cancel subscriptions, spend less on eating out, or save more each month. But I can see how it might make it easier for people who hesitate to look at their finances if they feel uncomfortable around spreadsheets. It didn't feel like math homework the way it often does.

Of course, it still relies on some trust in both OpenAI and Plaid, but as long as it's purely viewing and not actually touching the accounts, this could be a really useful, practical feature for ChatGPT users.

• A lawsuit claims RAM suppliers colluded to fix memory prices
• It says they did this by shifting manufacturing to higher-priced HBM
• But I doubt the lawsuit will make memory cheaper for consumers

Unless you’ve been living under a rock, you’ll probably know that prices of PC components are out of control right now. Costs of computer memory are some of the worst affected, making upgrading your PC next to impossible for all but the five richest kings of Europe.

Now, it seems that some people feel there is something deeply fishy about all these price rises. Indeed, a recently filed lawsuit has alleged that the globe’s leading RAM producers — Samsung, SK Hynix and Micron — colluded to deliberately constrain memory supply and push prices up as a result.

According to the filing (via AppleInsider), the three named companies shifted manufacturing capacity away from DRAM modules such as DDR3 and DDR4 — that is, the type of memory used in phones, computers, tablets and other consumer devices — towards HBM, which is used in artificial intelligence (AI) data centers and sells for a higher price. With lower supply, memory prices rose accordingly.

On the face of it, that's not illegal — companies are allowed to make strategic decisions to maximize profit if they wish. But what the lawsuit claims is that this move was a concerted, coordinated decision between the three firms, rather than each one separately responding to market conditions.

Because they have a stranglehold on the RAM market (up to 89% of DRAM market share and 100% of HBM market share, according to Counterpoint Research), the allegations of coordination — if proven — could amount to illegal behavior, potentially including price fixing.

Will this end the RAMpocalypse?

If you’re reading this and are desperate for some good news among all the PC component doom and gloom, you might be hoping that a favorable judgment in this lawsuit would open the door to lower memory prices and go some way towards getting costs back to reasonable levels.

Unfortunately, that outcome is unlikely. The lawsuit has not proven anything yet, and showing beyond doubt that Samsung, SK Hynix and Micron colluded to screw over consumers — rather than merely making independent decisions in response to the same crisis — will probably be exceedingly difficult to prove.

But even if the plaintiffs succeed in doing that, the lawsuit is likely to be a long, drawn-out process, with many appeals and potential reversals. And even then, it won’t change the reality that right now, prices are through the roof. We’re not getting any immediate relief, no matter what happens in the courtroom.

That skeptical view is widely reflected on social media. On Reddit, for example, user HorsePockets pointed out that HBM memory “pays way more than conventional DRAM and allows [the defendants] to transition away from being purely cyclical stocks.” Ramping up HBM production simply makes sense from a business perspective, they argued.

EloquentPinguin, meanwhile, put it this way: “If they truly think that DDR3 was wound down in favor of HBM, and not because it’s an almost 20-year-old technology with two well-established successive generations, then they might just be grasping [at] straws.”

That illustrates just how difficult this case could be to prove — and how little impact it might have on the ongoing RAM crisis. But the reality is that prices are completely out of control right now, whether or not memory manufacturers colluded to ensure that happened. Punishing the alleged culprits might feel cathartic, but it’s not going to put prices right any time soon. And that’s the real injustice here.

It’s clearly the wild, wild west when it comes to the race to build bigger and faster Artificial Intelligence systems.

The current backbone is built on Nvidia’s GPU architecture, allowing researchers and labs to build more and more sophisticated models that seem to leapfrog each other weekly.

The underlying technology required to achieve this is hundreds of thousands of these GPU cards sprawled across data centers throughout the US, drawing ever-increasing power and natural resources to push this frontier of human imagination.

In our rush towards our promise of a better future, are we laying the groundwork for this generation’s environmental calamity, much like previous generations had asbestos, microplastics, and lead?

Look no further than the SpaceX IPO, marketed as the largest IPO in history with a stunning $1.75 trillion initial valuation!

Buried deep inside, however, lies xAI – the AI company behind Grok. Grok runs from two massive data centers named Colossus 1 and 2.

What isn’t being discussed is that the NAACP is suing xAI for illegally installing air-polluting gas turbines in Mississippi that emit carcinogens into the air that residents breathe.

The question is not whether the power was needed, but what costs we are willing to accept, especially when, in this case, the demand releases toxic fumes into the air so people can interact with a chatbot.

An ominous situation

What makes the situation most ominous is that we are running a four-legged stool and waiting to see which breaks first. The first leg, of course, is computing power.

GPUs, while becoming more efficient, are at the same time getting denser and requiring more power. A traditional server rack used to draw about 5 kilowatts (kW) of power. GPUs upended that, with power draw climbing to 50 kW, 80 kW and now 140 kW per rack.

Indeed, Nvidia itself has proclaimed that its Kyber systems will draw an unheard-of 600 kW per rack by 2027. Whether this is true or not, the reality is that the power draw and density to run increasingly sophisticated hardware is going up; we just don’t know the timeframe of how we get there.

The second leg is what AI aficionados refer to as Jevon’s paradox. This is an economic principle that, as technology becomes cheaper to use and deploy, the use cases increase, so net consumption actually goes up even though the cost to run it keeps getting cheaper.

The third leg is that virtually all the free cash flow of megatech is going into deploying this IT infrastructure at an increasingly rapid rate. The projection for this year is $700 billion in capital expenditures, ballooning to over $1 trillion next year, and who knows after that.

At the same time, no one knows what limits Wall Street’s checkbook will be to fund additional expenditures for other companies. All this equipment must go into data centers somewhere.

Finally, we have a total lack of public policy on how to deploy these in sustainable ways, partially because there is a rush to get this equipment online, partly because no one really knows what the negative externalities could be, because no one has built up this much infrastructure at this scale and speed before.

The problem, of course, is if the part of the stool that deals with negative feedback to the environment and communities we live in is the first to break. It may not be easy to turn back once Pandora’s box has opened.

The need for policy sooner than later

Of course, we don’t have to wait for this all to happen. Thoughtful policy by the industry can get ahead of what could be a calamity by considering the “what ifs” before they happen.

Plus, by investing thoughtfully now, it stays ahead of what could be burdensome, time-consuming legislative efforts that lead to more regulation and compliance. In addition, consideration of things like total load (versus individual load) and the variance it might cause to the environment would be another consideration.

For example, a single independent data center building with a closed-loop water system may not draw that much water (other than initial fill, makeup needs, or during maintenance). However, if you start looking at the compound effects of how many additional buildings are going onto the campus, and how many campuses will be built in the next 10 years, then the impact could be considered before things go awry.

In the fast-moving race that is the AI space, the trickle-down effects on communities and the environment are something we should be looking at today.

Rather than letting companies run roughshod over people and cities, the data centers that they portend to inhabit can be good corporate citizens, which is important because these facilities typically have a useful life measured in decades.

The plan is for the data centers to be around for a long time, and so should everything else around them in a sustainable way.

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OpenAI's custom AI chip isn't just another attempt to loosen Nvidia's grip on AI hardware. It's the clearest sign yet that OpenAI is adopting the same vertically integrated strategy that transformed Apple over the past decade.

When OpenAI and Broadcom recently shared new details about Jalapeño, their custom inference processor, most of the discussion focused on Nvidia. Nvidia currently sits at the center of the AI industry, supplying the graphics processors that power everything from ChatGPT to image generators and coding assistants. Any attempt to reduce that dependence is naturally headline news.

For years, Apple has enjoyed a competitive advantage from making the most important parts of its products in-house. Instead of relying on someone else's processors or designing software around third-party hardware, it designed and built its own hardware and software. Competitors spent years trying to match that integration.

With its new custom inference processor, OpenAI appears to be building more than just an alternative chip. It's developing the same kind of vertically integrated ecosystem that helped transform Apple into one of the world's most valuable companies.

The chip is only part of the plan

When Apple introduced its M-series processors, the company aimed to build Macs that woke instantly and ran cool and quiet. Customers cared that everything simply felt smoother. OpenAI appears to be chasing a similar goal, even if the product is completely different.

Instead of laptops, it wants conversations that arrive faster. Building its own processor gives it another lever to pull that competitors relying entirely on third party hardware simply do not have.

Jalapeño is simply another piece of a much larger puzzle. The processor has been designed for inference rather than training. Training is the expensive process of creating an AI model as opposed to the inference done afterward. Every time someone asks ChatGPT a question, that's inference. Those billions of everyday interactions eventually become just as important as building the model itself because they determine both performance and operating costs.

Designing a processor specifically for those workloads gives OpenAI something that off-the-shelf hardware never fully can. It can begin tailoring the hardware around exactly how its own models think and respond, a more efficient method. And every improvement, whether in power consumption, speed, or networking, saves money and improves the AI experience.

OpenAI has been careful not to oversell the timeline, with broad deployment of the new chip still some way off. This is the beginning of a strategy rather than the final result.

Nvidia's challenge

Nvidia isn't going to panic right now, nor should it. Its processors still power much of today's AI boom. Demand continues to outstrip supply in many areas, and OpenAI itself remains one of its major customers. None of that changes because one new custom processor has appeared on the roadmap. What should catch Nvidia's attention is the pattern beyond OpenAI.

Google has spent years developing Tensor Processing Units. Amazon created Trainium and Inferentia. Microsoft has invested heavily in its own AI chips, as has Meta in custom accelerators for its expanding AI ambitions. OpenAI is now following the same path. Different companies have different technical goals, but they all seem to arrive at the same conclusion: as AI becomes a bigger part of their business, they don't want to depend entirely on someone else's hardware.

Of course, Apple designing its own processors certainly did not destroy Intel overnight. But there was a shift as Apple gained more control over pricing and product direction each time it replaced an external component with one of its own. The same could happen with AI.

Plus, OpenAI said its own AI models helped accelerate parts of the engineering process during chip development. AI is actually helping to make the hardware that will power its future iterations. That feedback loop may become increasingly important as chip design grows more complex. The future of AI may belong to the companies that own as much of the underlying machine as possible, regardless of where the models themselves rank.

If Apple's history is anything to go by, OpenAI is ready to be that company.

• Fitbit devices have a new fitness coach powered by Gemini AI
• But the AI seems to be giving people extremely questionable advice
• Users have voiced their dissatisfaction with the feature

Fitbit recently introduced a new fitness coach powered by Google’s Gemini artificial intelligence (AI), and it’s safe to say that it’s received a lot of flak from Fitbit users. Complaints have been flooding in, but surely few have been as bizarre as one recently posted to Reddit that involved some truly “unhinged” advice from Gemini.

Posting on Reddit, user bitteroldladybird started off by claiming that “The coach suggested I ditch my dog.” If that didn’t raise your eyebrows, what comes next surely will.

They continued by explaining that, “I’ve been walking my dog twice a day her whole life. Including the last year and a bit when I’ve had my Fitbit.”

But after that preamble, things start to get weird: “Recently the AI coach has been giving me feedback on my walks and it asked why my pace was so slow. I answered that I walk with my dog. This slows me down because she stops and sniffs and pees etc. Coach said it understood. Today it asked if I could ditch the dog to speed up my walks.” The user then opened the floor and asked fellow Redditors: “Has the coach given you weird or unhinged advice?”

Funnily enough, bitteroldladybird was far from the only person to relate a story like this. User KateJ95 recounted how “I got told to ditch my toddler … Turned coach off after that.” Individual_Sun2060, on the other hand, said “My coach incessantly tells me to rest, and has probably suggested I take the day off EVERY SINGLE DAY.”

User vemailangah, meanwhile, had a helpful suggestion for Fitbit’s next update: “coach sends AI robot to get rid of the dog to help you improve your walks.”

TechRadar’s own Matt Evans has had a similarly bizarre experience with Fitbit’s AI coach, explaining that it developed an obsession with a minor cold and wouldn’t let the issue go. After Evans didn’t wear his Fitbit for one day — and therefore logged zero steps or workouts — the AI chimed in with: “yesterday was a full recovery day with minimal movement.”

As Matt explained, it seemed that Gemini “really thought I spend 12 hours lying perfectly still, like a mummy in a sarcophagus.”

Latching onto any context

Judging by the feedback from people on Reddit and here at TechRadar, it seems that Fitbit’s AI coach is tuned a little too strongly towards fitness efficiency and improvement. If it detects any sort of “hindrance” that it feels is slowing you down, it suggests jettisoning it at the earliest opportunity — even if that means ditching your beloved pup.

TechRadar’s Evans points out that this behavior could be because Gemini “just kind of latches on to any context you give it, and is designed to improve your health — occasionally to its detriment when it comes to subtlety and context.” Because you know far more about yourself than Gemini does, the chatbot has to take any cue it can in order to build a picture of your wellbeing. And if you mention something tangentially relevant in your life, Gemini has a few other resources for context.

Aside from creating the kinds of bizarre situations that we’ve seen here, this issue limits the fitness coach’s utility. Another thread on Reddit asked “Does anyone actually use the AI Coach?” and was filled with replies from people who have lost patience with the feature. “When the trial ends, I’m out. Coach is garbage,” said flanga, while realManTing shared that “I find myself yelling at it over text and I cannot wait for my trial to end.”

As the original poster in that thread put it, the coach “constantly gives me long walls of text that are either obvious, outdated or just not useful. I don’t want to read an essay every time I open the app — I just want short, actionable insights.”

It therefore seems clear that Gemini’s AI coach is not particularly popular among Fitbit users and has a worrying tendency to offer questionable advice and to irritate them. Hopefully Google can make some rapid improvements before it suggests anyone else dump their dog to record a slightly faster walk.

• Real-world impact is more important than benchmark scores, Meta AI VP says
• Song emphasizes security, trust and real-world benefits
• Meta's latest model prioritizes people, she says

Meta's newly appointed AI research chief, Dawn Song, is betting on agentic AI for the future of artificial intelligence, emphasizing they should augment humans rather than replace people altogether.

Song sees agents performing "economically valuable" tasks like repetitive and time-consuming work, ultimately freeing up humans to do more creative work.

While companies struggle to quantify AI's impacts and deliver a meaningful ROI, Song believes the focus should be real-world impact rather than benchmark scores.

AI agents should augment – not replace – humans

"The goal is not to replace humans," Song told the South China Morning Post. She confirmed she would be joining Meta Superintelligence Labs in a LinkedIn post, together with other team members from Virtue AI.

"[AI] must be secure, trustworthy, and beneficial," she added.

Song is also a professor in computer science at the University of California, Berkeley – a university that recently introduced Agents' Last Exam (ALE), a new type of benchmark that assesses whether AI agents can complete more than 1,500 economically valuable tasks across 55 different industries.

Meta itself launched its first new model, Muse, in April, which it says is designed to "prioritize people."

As MSL's Vice President of AI Research, Song will focus on AI safety, security and research, and will likely continue to emphasize the role of humans in an AI-first era.

Model capabilities are no longer a drawback for AI developers, with human, socioeconomic and geopolitical impacts now emerging as a major focus. Anthropic was recently forced by the White House to pull its latest frontier models over jailbreaking concerns.

Anthropic has accused groups linked to Alibaba and its Qwen AI lab of carrying out a massive campaign to extract capabilities from Claude just by asking it a lot of questions, as first reported by Reuters. The AI developer wrote a letter to U.S. lawmakers alleging that Alibaba used nearly 25,000 fraudulent accounts to generate more than 28.8 million interactions and glean detailed, proprietary information about Claude.

Alibaba has not publicly responded to the allegations, and there has been no independent confirmation of Anthropic's claims, but simply leveling them has potentially enormous consequences. The sheer volume of accounts and interactions is eye-catching, but it's even more fascinating how it reveals a vulnerability in AI models that can give away their secrets.

AI developers may now have to worry that rivals can learn from those models without ever seeing the underlying code or training data through a technique known as model distillation. Essentially, AI models will inadvertently share deliberately obscured facts about themselves if a huge number of the right questions are asked. As an analogy, imagine taking a test about a book, but instead of reading the book, you ask the author one million questions about their life, their thinking, their experience writing the book, and several hundred thousand more questions. You'd probably have a pretty good chance of knowing everything they might have written without once cracking the covers.

Can you copy an AI just by talking to it?

Model distillation is a common technique used by AI companies to build variations of their models, especially smaller, faster options. But no company would be okay with a rival using their model to train the competition. But that's what Anthropic alleges. The fake accounts supposedly asked Claude a ton of very complex and detailed questions related to its advanced software engineering and agentic reasoning features. The responses filled in a picture of the model's workings, accelerating Alibaba's own development of competing AI systems, Anthropic claimed.

The conundrum is obvious. Large language models are designed to answer questions. Every answer teaches the user something about how the model behaves. You can't interact with an AI model, or a person, without giving up some information about yourself. Normally, that wouldn't matter, but at the scale Anthropic is claiming, conversations become reverse engineering.

It's not the first time Anthropic has alleged illicit model distillation. Anthropic levied similar claims against DeepSeek, Moonshot AI, and MiniMax earlier this year. And other companies, including OpenAI, have expressed concern that they have also been victims of the technique.

The glaring irony that the companies that used enormous collections of publicly available information, including licensed material, to train their AI models are now arguing about how those same models are valuable intellectual property is hard to ignore.

AI arms race

AI developers see their models' behavior as crucial to competing with rivals. If another company can reproduce much of that behavior by asking enough carefully designed questions, spending billions of dollars training frontier models starts to seem like a waste.

Anthropic claims model distillation can effectively transfer years of work on their part to another company for almost nothing. Anthropic asked lawmakers to take action and combat this problem as soon as possible. If leading models can be imitated so easily, there won't be much incentive to innovate, and the AI competition will only be about beating copycats. And picking the best models will be difficult, as a new AI model that matches an existing one's capabilities might be born of years of original research or simply copying an existing option.

Whether Anthropic ultimately proves its allegations, they have revealed that the next great AI battle may not be about building the smartest model. It may be about stopping somebody else from talking to your model and learning how it operates, one question at a time.